Tootfinder

Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy
Yuhao Liu, Tengfei Wang, Fang Liu, Zhenwei Wang, Rynson W. H. Lau
https://arxiv.org/abs/2506.22432

Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy
Recent advances in deep generative modeling have unlocked unprecedented opportunities for video synthesis. In real-world applications, however, users often seek tools to faithfully realize their creative editing intentions with precise and consistent control. Despite the progress achieved by existing methods, ensuring fine-grained alignment with user intentions remains an open and challenging problem. In this work, we present Shape-for-Motion, a novel framework that incorporates a 3D proxy for …

Shape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow
Anuraj Maurya
https://arxiv.org/abs/2507.22082 https://arxiv.org/pdf/2507.2…

Shape Invariant 3D-Variational Autoencoder: Super Resolution in Turbulence flow
Deep learning provides a versatile suite of methods for extracting structured information from complex datasets, enabling deeper understanding of underlying fluid dynamic phenomena. The field of turbulence modeling, in particular, benefits from the growing availability of high-dimensional data obtained through experiments, field observations, and large-scale simulations spanning multiple spatio-temporal scales. This report presents a concise overview of both classical and deep learningbased app…

This https://arxiv.org/abs/2405.14979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding t…

3Description: An Intuitive Human-AI Collaborative 3D Modeling Approach
Zhuodi Cai
https://arxiv.org/abs/2506.21845 https://arxiv.org/…

DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
Yuliang Huang, Imraj Singh, Thomas Joyce, Kris Thielemans, Jamie R. McClelland
https://arxiv.org/abs/2506.22280

DIGS: Dynamic CBCT Reconstruction using Deformation-Informed 4D Gaussian Splatting and a Low-Rank Free-Form Deformation Model
3D Cone-Beam CT (CBCT) is widely used in radiotherapy but suffers from motion artifacts due to breathing. A common clinical approach mitigates this by sorting projections into respiratory phases and reconstructing images per phase, but this does not account for breathing variability. Dynamic CBCT instead reconstructs images at each projection, capturing continuous motion without phase sorting. Recent advancements in 4D Gaussian Splatting (4DGS) offer powerful tools for modeling dynamic scenes, …

SonicGauss: Position-Aware Physical Sound Synthesis for 3D Gaussian Representations
Chunshi Wang, Hongxing Li, Yawei Luo
https://arxiv.org/abs/2507.19835 https://

SonicGauss: Position-Aware Physical Sound Synthesis for 3D Gaussian Representations
While 3D Gaussian representations (3DGS) have proven effective for modeling the geometry and appearance of objects, their potential for capturing other physical attributes-such as sound-remains largely unexplored. In this paper, we present a novel framework dubbed SonicGauss for synthesizing impact sounds from 3DGS representations by leveraging their inherent geometric and material properties. Specifically, we integrate a diffusion-based sound synthesis model with a PointTransformer-based featu…

SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity
Xingyang Li, Jie Jiang, Yu Feng, Yiming Gan, Jieru Zhao, Zihan Liu, Jingwen Leng, Minyi Guo
https://arxiv.org/abs/2507.21499

SLTarch: Towards Scalable Point-Based Neural Rendering by Taming Workload Imbalance and Memory Irregularity
Rendering is critical in fields like 3D modeling, AR/VR, and autonomous driving, where high-quality, real-time output is essential. Point-based neural rendering (PBNR) offers a photorealistic and efficient alternative to conventional methods, yet it is still challenging to achieve real-time rendering on mobile platforms. We pinpoint two major bottlenecks in PBNR pipelines: LoD search and splatting. LoD search suffers from workload imbalance and irregular memory access, making it inefficient on …

HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars
Byungjun Kim, Shunsuke Saito, Giljoo Nam, Tomas Simon, Jason Saragih, Hanbyul Joo, Junxuan Li
https://arxiv.org/abs/2507.19481

HairCUP: Hair Compositional Universal Prior for 3D Gaussian Avatars
We present a universal prior model for 3D head avatars with explicit hair compositionality. Existing approaches to build generalizable priors for 3D head avatars often adopt a holistic modeling approach, treating the face and hair as an inseparable entity. This overlooks the inherent compositionality of the human head, making it difficult for the model to naturally disentangle face and hair representations, especially when the dataset is limited. Furthermore, such holistic models struggle to su…

A Black Hole is Born: 3D GRMHD Simulation of Black Hole Formation from Core-Collapse
Goni Halevi, Swapnil Shankar, Philipp M\"osta, Roland Haas, Erik Schnetter
https://arxiv.org/abs/2506.20837

A Black Hole is Born: 3D GRMHD Simulation of Black Hole Formation from Core-Collapse
We present the first three-dimensional, fully general-relativistic magnetohydrodynamic (3D GRMHD) simulation of a black hole (BH) formed from the collapsed core of a massive star. The ability to self-consistently capture the birth of a compact remnant in 3D is crucial for modeling natal BH properties (including masses, spins, and kicks), which are of particular interest in the era of gravitational wave astronomy. However, such simulations have remained elusive due to extreme computational chall…

ReSem3D: Refinable 3D Spatial Constraints via Fine-Grained Semantic Grounding for Generalizable Robotic Manipulation
Chenyu Su, Weiwei Shang, Chen Qian, Fei Zhang, Shuang Cong
https://arxiv.org/abs/2507.18262

ReSem3D: Refinable 3D Spatial Constraints via Fine-Grained Semantic Grounding for Generalizable Robotic Manipulation
Semantics-driven 3D spatial constraints align highlevel semantic representations with low-level action spaces, facilitating the unification of task understanding and execution in robotic manipulation. The synergistic reasoning of Multimodal Large Language Models (MLLMs) and Vision Foundation Models (VFMs) enables cross-modal 3D spatial constraint construction. Nevertheless, existing methods have three key limitations: (1) coarse semantic granularity in constraint modeling, (2) lack of real-time…

3DGH: 3D Head Generation with Composable Hair and Face
Chengan He, Junxuan Li, Tobias Kirschstein, Artem Sevastopolsky, Shunsuke Saito, Qingyang Tan, Javier Romero, Chen Cao, Holly Rushmeier, Giljoo Nam
https://arxiv.org/abs/2506.20875

3DGH: 3D Head Generation with Composable Hair and Face
We present 3DGH, an unconditional generative model for 3D human heads with composable hair and face components. Unlike previous work that entangles the modeling of hair and face, we propose to separate them using a novel data representation with template-based 3D Gaussian Splatting, in which deformable hair geometry is introduced to capture the geometric variations across different hairstyles. Based on this data representation, we design a 3D GAN-based architecture with dual generators and empl…

Music-Aligned Holistic 3D Dance Generation via Hierarchical Motion Modeling
Xiaojie Li, Ronghui Li, Shukai Fang, Shuzhao Xie, Xiaoyang Guo, Jiaqing Zhou, Junkun Peng, Zhi Wang
https://arxiv.org/abs/2507.14915

Music-Aligned Holistic 3D Dance Generation via Hierarchical Motion Modeling
Well-coordinated, music-aligned holistic dance enhances emotional expressiveness and audience engagement. However, generating such dances remains challenging due to the scarcity of holistic 3D dance datasets, the difficulty of achieving cross-modal alignment between music and dance, and the complexity of modeling interdependent motion across the body, hands, and face. To address these challenges, we introduce SoulDance, a high-precision music-dance paired dataset captured via professional motio…

Steady-State Heating of Diffuse Coronal Plasma in a Solar Active Region
Gregory D. Fleishman, Alexey A. Kuznetsov, Gelu M. Nita
https://arxiv.org/abs/2506.18723

Steady-State Heating of Diffuse Coronal Plasma in a Solar Active Region
Solar corona is much hotter than lower layers of the solar atmosphere-photosphere and chromosphere. The coronal temperature is up to 1MK in quiet sun areas, while up to several MK in active regions, which implies a key role of magnetic field in coronal heating. This means that understanding coronal heating requires reliable modeling of the underlying three-dimensional (3D) magnetic structure of an active region validated by observations. Here we employ synergy between 3D modeling, optically thi…

MADrive: Memory-Augmented Driving Scene Modeling
Polina Karpikova, Daniil Selikhanovych, Kirill Struminsky, Ruslan Musaev, Maria Golitsyna, Dmitry Baranchuk
https://arxiv.org/abs/2506.21520

MADrive: Memory-Augmented Driving Scene Modeling
Recent advances in scene reconstruction have pushed toward highly realistic modeling of autonomous driving (AD) environments using 3D Gaussian splatting. However, the resulting reconstructions remain closely tied to the original observations and struggle to support photorealistic synthesis of significantly altered or novel driving scenarios. This work introduces MADrive, a memory-augmented reconstruction framework designed to extend the capabilities of existing scene reconstruction methods by r…

Replaced article(s) found for physics.plasm-ph. https://arxiv.org/list/physics.plasm-ph/new
[1/1]:
- ThinCurr: An open-source 3D thin-wall eddy current modeling code for the analysis of large-scale ...
Hansen, Battey, Braun, Miller, Lagieski, Stewart, Sweeney, Paz-Soldan

This https://arxiv.org/abs/2505.21041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV based inspection, and smart city digital twins. While aerial imagery offers broad coverage and complements limitations of ground-based data, reconstructing city-scale environments from such views remains challenging due to occlusions, incomplete geometry, and high memory demands. Recent advances like 3D Gaussian Splatting (3DGS) improve scalability and visual quality but remain li…

A validated coupled three-dimensional hydrodynamic and spectral wind-wave model for the western north Atlantic Ocean
Maria Venolia, Reza Marsooli, Jaime R. Calzada
https://arxiv.org/abs/2505.18803

A validated coupled three-dimensional hydrodynamic and spectral wind-wave model for the western north Atlantic Ocean
Wind-wave and ocean current interactions affect critical coastal and oceanic processes, yet modeling these interactions presents significant challenges. The western North Atlantic Ocean provides an ideal test environment for coupled hydrodynamics and wind wave models, thanks to its energetic surface currents such as the Gulf Stream. This study evaluates a high-resolution coupled SCHISM WWM III model, utilizing NOAA's 'STOFS-3D-Atlantic' computational mesh, while incorporating three-dimensional …

Testing halo models for constraining astrophysical feedback with multi-probe modeling: I. 3D Power spectra and mass fractions
Pranjal R. S., Shivam Pandey, Dhayaa Anbajagane, Elisabeth Krause, Klaus Dolag
https://arxiv.org/abs/2507.13317

Testing halo models for constraining astrophysical feedback with multi-probe modeling: I. 3D Power spectra and mass fractions
Upcoming Stage-IV surveys will deliver measurements of distribution of matter with unprecedented precision, demanding highly accurate theoretical models for cosmological parameter inference. A major source of modeling uncertainty lies in astrophysical processes associated with galaxy formation and evolution, which remain poorly understood. Probes such as the thermal and kinematic Sunyaev-Zel'dovich effects, X-rays, and dispersion measure from fast radio bursts offer a promising avenue for mappi…

Reconstructing 4D Spatial Intelligence: A Survey
Yukang Cao, Jiahao Lu, Zhisheng Huang, Zhuowei Shen, Chengfeng Zhao, Fangzhou Hong, Zhaoxi Chen, Xin Li, Wenping Wang, Yuan Liu, Ziwei Liu
https://arxiv.org/abs/2507.21045

Reconstructing 4D Spatial Intelligence: A Survey
Reconstructing 4D spatial intelligence from visual observations has long been a central yet challenging task in computer vision, with broad real-world applications. These range from entertainment domains like movies, where the focus is often on reconstructing fundamental visual elements, to embodied AI, which emphasizes interaction modeling and physical realism. Fueled by rapid advances in 3D representations and deep learning architectures, the field has evolved quickly, outpacing the scope of …

Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling
Yilin Lyu, Fan Yang, Xiaoyue Liu, Zichen Jiang, Joshua Dillon, Debbie Zhao, Martyn Nash, Charlene Mauger, Alistair Young, Ching-Hui Sia, Mark YY Chan, Lei Li
https://arxiv.org/abs/2507.15194

Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI with Explicit Cardiac Motion Modeling
Accurate representation of myocardial infarct geometry is crucial for patient-specific cardiac modeling in MI patients. While Late gadolinium enhancement (LGE) MRI is the clinical gold standard for infarct detection, it requires contrast agents, introducing side effects and patient discomfort. Moreover, infarct reconstruction from LGE often relies on sparsely sampled 2D slices, limiting spatial resolution and accuracy. In this work, we propose a novel framework for automatically reconstructing …

Learning-Based Modeling of a Magnetically Steerable Soft Suction Device for Endoscopic Endonasal Interventions
Majid Roshanfar, Alex Zhang, Changyan He, Amir Hooshiar, Dale J. Podolsky, Thomas Looi, Eric Diller
https://arxiv.org/abs/2507.15155

Learning-Based Modeling of a Magnetically Steerable Soft Suction Device for Endoscopic Endonasal Interventions
This letter introduces a novel learning-based modeling framework for a magnetically steerable soft suction device designed for endoscopic endonasal brain tumor resection. The device is miniaturized (4 mm outer diameter, 2 mm inner diameter, 40 mm length), 3D printed using biocompatible SIL 30 material, and integrates embedded Fiber Bragg Grating (FBG) sensors for real-time shape feedback. Shape reconstruction is represented using four Bezier control points, enabling a compact and smooth model o…

CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Weihang Liu, Yuhui Zhong, Yuke Li, Xi Chen, Jiadi Cui, Honglong Zhang, Lan Xu, Xin Lou, Yujiao Shi, Jingyi Yu, Yingliang Zhang
https://arxiv.org/abs/2505.21041

CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV based inspection, and smart city digital twins. While aerial imagery offers broad coverage and complements limitations of ground-based data, reconstructing city-scale environments from such views remains challenging due to occlusions, incomplete geometry, and high memory demands. Recent advances like 3D Gaussian Splatting (3DGS) improve scalability and visual quality but remain li…

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
Yiwen Chen, Zhihao Li, Yikai Wang, Hu Zhang, Qin Li, Chi Zhang, Guosheng Lin
https://arxiv.org/abs/2507.17745

Ultra3D: Efficient and High-Fidelity 3D Generation with Part Attention
Recent advances in sparse voxel representations have significantly improved the quality of 3D content generation, enabling high-resolution modeling with fine-grained geometry. However, existing frameworks suffer from severe computational inefficiencies due to the quadratic complexity of attention mechanisms in their two-stage diffusion pipelines. In this work, we propose Ultra3D, an efficient 3D generation framework that significantly accelerates sparse voxel modeling without compromising quali…

Modeling Gravitational Wave Bias from 3D Power Spectra of Spectroscopic Surveys
Dorsa Sadat Hosseini, Amir Dehghani, J. Leo Kim, Alex Krolewski, Suvodip Mukherjee, Ghazal Geshnizjani
https://arxiv.org/abs/2506.11201

Modeling Gravitational Wave Bias from 3D Power Spectra of Spectroscopic Surveys
We present a framework for relating gravitational wave (GW) sources to the astrophysical properties of spectroscopic galaxy samples. We show how this can enable using clustering measurements of gravitational wave (GW) sources to infer the relationship between the GW sources and the astrophysical properties of their host galaxies. We accomplish this by creating mock GW catalogs from the spectroscopic Sloan Digital Sky Survey (SDSS) DR7 galaxy survey. We populate the GWs using a joint host-galaxy…

HybridTM: Combining Transformer and Mamba for 3D Semantic Segmentation
Xinyu Wang, Jinghua Hou, Zhe Liu, Yingying Zhu
https://arxiv.org/abs/2507.18575 https://

HybridTM: Combining Transformer and Mamba for 3D Semantic Segmentation
Transformer-based methods have demonstrated remarkable capabilities in 3D semantic segmentation through their powerful attention mechanisms, but the quadratic complexity limits their modeling of long-range dependencies in large-scale point clouds. While recent Mamba-based approaches offer efficient processing with linear complexity, they struggle with feature representation when extracting 3D features. However, effectively combining these complementary strengths remains an open challenge in thi…

Uncertainty-aware Probabilistic 3D Human Motion Forecasting via Invertible Networks
Yue Ma, Kanglei Zhou, Fuyang Yu, Frederick W. B. Li, Xiaohui Liang
https://arxiv.org/abs/2507.14694

Uncertainty-aware Probabilistic 3D Human Motion Forecasting via Invertible Networks
3D human motion forecasting aims to enable autonomous applications. Estimating uncertainty for each prediction (i.e., confidence based on probability density or quantile) is essential for safety-critical contexts like human-robot collaboration to minimize risks. However, existing diverse motion forecasting approaches struggle with uncertainty quantification due to implicit probabilistic representations hindering uncertainty modeling. We propose ProbHMI, which introduces invertible networks to p…

PGD-based optimization of 3D bobsleigh track centerlines from 2D centerlines for simulation applications
Zhe Chen, Huichao Zhao, Yongfeng Jiang, Minghui Bai, Lun Li, Jicheng Chen
https://arxiv.org/abs/2507.08393

PGD-based optimization of 3D bobsleigh track centerlines from 2D centerlines for simulation applications
The centerline of a bobsleigh track defines its geometry and is essential for simulation modeling. To reduce bBobsleigh training costs, leveraging the centerline of the bobsleigh track to construct a virtual environment that closely replicates real competitive settings presents a promising solution. However, publicly available centerline data are typically limited and it is imprecise to construct a training system solely based on 2-dimensional (2D) centerline. To address this practical issue, t…

Pinching-Antenna Systems For Indoor Immersive Communications: A 3D-Modeling Based Performance Analysis
Yulei Wang, Yalin Liu, Yaru Fu, Zhiguo Ding
https://arxiv.org/abs/2506.07771

Pinching-Antenna Systems For Indoor Immersive Communications: A 3D-Modeling Based Performance Analysis
The emerging pinching antenna (PA) technology has high flexibility to reconfigure wireless channels and combat line-of-sight blockage, thus holding transformative potential for indoor immersive applications in 6G. This paper investigates Pinching-antenna systems (PASS) for indoor immersive communications. Our contributions are threefold: (1) we construct a 3D model to characterize the distribution of users, waveguides, and PAs in the PASS; (2) we develop a general theoretical model on downlink …

Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling
Paula Feldman, Miguel Fainstein, Viviana Siless, Claudio Delrieux, Emmanuel Iarussi
https://arxiv.org/abs/2506.14914

Recursive Variational Autoencoders for 3D Blood Vessel Generative Modeling
Anatomical trees play an important role in clinical diagnosis and treatment planning. Yet, accurately representing these structures poses significant challenges owing to their intricate and varied topology and geometry. Most existing methods to synthesize vasculature are rule based, and despite providing some degree of control and variation in the structures produced, they fail to capture the diversity and complexity of actual anatomical data. We developed a Recursive variational Neural Network…

Determining the Validity of Tokamak Perturbed Equilibrium Modeling Using Nonlinear Equilibria
J. Halpern, N. C. Logan, E. Paul, C. Paz-Soldan
https://arxiv.org/abs/2506.14938

Determining the Validity of Tokamak Perturbed Equilibrium Modeling Using Nonlinear Equilibria
The prediction of perturbed equilibrium models for tokamaks with small 3D fields is strongly dependent on which reference frame for axisymmetry is assumed - for example, the toroidal field (TF) coil vs the poloidal field (PF) coil centroid. We use fully 3D equilibria generated by VMEC to determine the correct reference frame in these models when subject to n=1 coil asymmetries. We analyze a case for SPARC and find that the appropriate frame can be well approximated by the centroid of the TF coi…

PhysX: Physical-Grounded 3D Asset Generation
Ziang Cao, Zhaoxi Chen, Linag Pan, Ziwei Liu
https://arxiv.org/abs/2507.12465 https://ar…

PhysX: Physical-Grounded 3D Asset Generation
3D modeling is moving from virtual to physical. Existing 3D generation primarily emphasizes geometries and textures while neglecting physical-grounded modeling. Consequently, despite the rapid development of 3D generative models, the synthesized 3D assets often overlook rich and important physical properties, hampering their real-world application in physical domains like simulation and embodied AI. As an initial attempt to address this challenge, we propose \textbf{PhysX}, an end-to-end paradi…

Feasibility of spectral-element modeling of wave propagation through the anatomy of marine mammals
Carlos Garc\'ia A., Vladimiro Boselli, Aida Hejazi Nooghabi, Andrea Colombi, Lapo Boschi
https://arxiv.org/abs/2506.22944

Feasibility of spectral-element modeling of wave propagation through the anatomy of marine mammals
This study introduces the first 3D spectral-element method (SEM) simulation of ultrasonic wave propagation in a bottlenose dolphin (Tursiops truncatus) head. Unlike traditional finite-element methods (FEM), which struggle with high-frequency simulations due to costly linear-system inversions and slower convergence, SEM offers exponential convergence and efficient parallel computation. Using Computed Tomography (CT) scan data, we developed a detailed hexahedral mesh capturing complex anatomical …

Coupling 1D stellar evolution with 3D-hydrodynamical simulations on-the-fly III: stellar evolution at different metallicities
Yixiao Zhou, Jakob L. R{\o}rsted, Achim Weiss, Andreas C. S. J{\o}rgensen, Cis Lagae, Luisa F. Rodr\'iguez D\'iaz, Yaguang Li, Mark L. Winther, Jens R. Larsen, J{\o}rgen Christensen-Dalsgaard, Oleg Kochukhov, Karen R. Pollard, Tanda Li

Coupling 1D stellar evolution with 3D-hydrodynamical simulations on-the-fly III: stellar evolution at different metallicities
A major weakness in one-dimensional (1D) stellar structure and evolution modeling is the simplified treatment of convection, which leads to erroneous near-surface stratification and considerable uncertainties in predicted effective temperatures and luminosities of low-mass stars. In a series of preceding works, a novel method for coupling 1D stellar structural models with a grid of 3D surface convection simulations during stellar evolution was developed, at solar metallicity. This 1D-3D couplin…

Replaced article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- Music-Aligned Holistic 3D Dance Generation via Hierarchical Motion Modeling
Xiaojie Li, Ronghui Li, Shukai Fang, Shuzhao Xie, Xiaoyang Guo, Jiaqing Zhou, Junkun Peng, Zhi Wang

A Voxel-Wise Uncertainty-Guided Framework for Glioma Segmentation Using Spherical Projection-Based U-Net and Localized Refinement in Multi-Parametric MRI
Zhenyu Yang, Chen Yang, Rihui Zhang, Minbin Chen, Chunhao Wang, Fang-Fang Yin
https://arxiv.org/abs/2507.15622

A Voxel-Wise Uncertainty-Guided Framework for Glioma Segmentation Using Spherical Projection-Based U-Net and Localized Refinement in Multi-Parametric MRI
Purpose: Accurate segmentation of glioma subregions in multi-parametric MRI (MP-MRI) is essential for diagnosis and treatment planning but remains challenging due to tumor heterogeneity and ambiguous boundaries. This study proposes an uncertainty-guided hybrid framework integrating spherical projection-based 2D modeling with targeted 3D refinement to enhance segmentation accuracy and interpretability. Methods: Using the BraTS2020 dataset (369 patients, four-modality MP-MRI), three 2D U-Nets wer…

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning
Chuxin Wang, Yixin Zha, Wenfei Yang, Tianzhu Zhang
https://arxiv.org/abs/2506.21541

StruMamba3D: Exploring Structural Mamba for Self-supervised Point Cloud Representation Learning
Recently, Mamba-based methods have demonstrated impressive performance in point cloud representation learning by leveraging State Space Model (SSM) with the efficient context modeling ability and linear complexity. However, these methods still face two key issues that limit the potential of SSM: Destroying the adjacency of 3D points during SSM processing and failing to retain long-sequence memory as the input length increases in downstream tasks. To address these issues, we propose StruMamba3D,…

Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding
Yimin Dou, Xinming Wu, Nathan L Bangs, Harpreet Singh Sethi, Jintao Li, Hang Gao, Zhixiang Guo
https://arxiv.org/abs/2507.00419

Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding
Understanding Earth's subsurface is critical for energy transition, natural hazard mitigation, and planetary science. Yet subsurface analysis remains fragmented, with separate models required for structural interpretation, stratigraphic analysis, geobody segmentation, and property modeling-each tightly coupled to specific data distributions and task formulations. We introduce the Geological Everything Model 3D (GEM), a unified generative architecture that reformulates all these tasks as prompt-…

DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
Hangyu Ji
https://arxiv.org/abs/2506.05297 https://

DM-SegNet: Dual-Mamba Architecture for 3D Medical Image Segmentation with Global Context Modeling
Accurate 3D medical image segmentation demands architectures capable of reconciling global context modeling with spatial topology preservation. While State Space Models (SSMs) like Mamba show potential for sequence modeling, existing medical SSMs suffer from encoder-decoder incompatibility: the encoder's 1D sequence flattening compromises spatial structures, while conventional decoders fail to leverage Mamba's state propagation. We present DM-SegNet, a Dual-Mamba architecture integrating direct…

Beyond the Plane: A 3D Representation of Human Personal Space for Socially-Aware Robotics
Caio C. G. Ribeiro, Douglas G. Macharet
https://arxiv.org/abs/2506.13937

Beyond the Plane: A 3D Representation of Human Personal Space for Socially-Aware Robotics
The increasing presence of robots in human environments requires them to exhibit socially appropriate behavior, adhering to social norms. A critical aspect in this context is the concept of personal space, a psychological boundary around an individual that influences their comfort based on proximity. This concept extends to human-robot interaction, where robots must respect personal space to avoid causing discomfort. While much research has focused on modeling personal space in two dimensions, …

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Xintong Zou, Zhijie Li, Yunpeng Wang, Huiyu Yang, Jianchun Wang
https://arxiv.org/abs/2506.04898

Uncertainty quantification and stability of neural operators for prediction of three-dimensional turbulence
Turbulence poses challenges for numerical simulation due to its chaotic, multiscale nature and high computational cost. Traditional turbulence modeling often struggles with accuracy and long-term stability. Recent scientific machine learning (SciML) models, such as Fourier Neural Operators (FNO), show promise in solving PDEs, but are typically limited to one-step-ahead predictions and often fail over long time horizons, especially in 3D turbulence. This study proposes a framework to assess the …

This https://arxiv.org/abs/2505.07286 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Piloting Structure-Based Drug Design via Modality-Specific Optimal Schedule
Structure-Based Drug Design (SBDD) is crucial for identifying bioactive molecules. Recent deep generative models are faced with challenges in geometric structure modeling. A major bottleneck lies in the twisted probability path of multi-modalities -- continuous 3D positions and discrete 2D topologies -- which jointly determine molecular geometries. By establishing the fact that noise schedules decide the Variational Lower Bound (VLB) for the twisted probability path, we propose VLB-Optimal Sche…

ArtGS:3D Gaussian Splatting for Interactive Visual-Physical Modeling and Manipulation of Articulated Objects
Qiaojun Yu, Xibin Yuan, Yu jiang, Junting Chen, Dongzhe Zheng, Ce Hao, Yang You, Yixing Chen, Yao Mu, Liu Liu, Cewu Lu
https://arxiv.org/abs/2507.02600

ArtGS:3D Gaussian Splatting for Interactive Visual-Physical Modeling and Manipulation of Articulated Objects
Articulated object manipulation remains a critical challenge in robotics due to the complex kinematic constraints and the limited physical reasoning of existing methods. In this work, we introduce ArtGS, a novel framework that extends 3D Gaussian Splatting (3DGS) by integrating visual-physical modeling for articulated object understanding and interaction. ArtGS begins with multi-view RGB-D reconstruction, followed by reasoning with a vision-language model (VLM) to extract semantic and structura…

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Haoyu Wu, Diankun Wu, Tianyu He, Junliang Guo, Yang Ye, Yueqi Duan, Jiang Bian
https://arxiv.org/abs/2507.07982

Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling
Videos inherently represent 2D projections of a dynamic 3D world. However, our analysis suggests that video diffusion models trained solely on raw video data often fail to capture meaningful geometric-aware structure in their learned representations. To bridge this gap between video diffusion models and the underlying 3D nature of the physical world, we propose Geometry Forcing, a simple yet effective method that encourages video diffusion models to internalize latent 3D representations. Our ke…

NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling
Pengfei Wang, Qiujie Dong, Fangtian Liang, Hao Pan, Lei Yang, Congyi Zhang, Guying Lin, Caiming Zhang, Yuanfeng Zhou, Changhe Tu, Shiqing Xin, Alla Sheffer, Xin Li, Wenping Wang
https://arxiv.org/abs/2506.13050

NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling
Neural implicit shape representation has drawn significant attention in recent years due to its smoothness, differentiability, and topological flexibility. However, directly modeling the shape of a neural implicit surface, especially as the zero-level set of a neural signed distance function (SDF), with sparse geometric control is still a challenging task. Sparse input shape control typically includes 3D curve networks or, more generally, 3D curve sketches, which are unstructured and cannot be …

Conversational Interfaces for Parametric Conceptual Architectural Design: Integrating Mixed Reality with LLM-driven Interaction
Ruochen Ji, Lyu Tiangang
https://arxiv.org/abs/2506.06066

Conversational Interfaces for Parametric Conceptual Architectural Design: Integrating Mixed Reality with LLM-driven Interaction
Mixed reality (MR) environments offer embodied spatial interaction, providing intuitive 3D manipulation capabilities that enhance the conceptual design process. Parametric modeling, a powerful and advanced architectural design method, enables the generation of complex, optimized geometries. However, its integration into MR environments remains limited due to precision constraints and unsuitable input modalities. Existing MR tools prioritize spatial interaction but lack the control and expressiv…

Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
Szymon P{\l}otka, Maciej Chrabaszcz, Gizem Mert, Ewa Szczurek, Arkadiusz Sitek
https://arxiv.org/abs/2507.06363

Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation
In recent years, artificial intelligence has significantly advanced medical image segmentation. However, challenges remain, including efficient 3D medical image processing across diverse modalities and handling data variability. In this work, we introduce Hierarchical Soft Mixture-of-Experts (HoME), a two-level token-routing layer for efficient long-context modeling, specifically designed for 3D medical image segmentation. Built on the Mamba state-space model (SSM) backbone, HoME enhances seque…

Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation
Chengan He, Jorge Alejandro Amador Herrera, Zhixin Shu, Xin Sun, Yao Feng, S\"oren Pirk, Dominik L. Michels, Meng Zhang, Tuanfeng Y. Wang, Julie Dorsey, Holly Rushmeier, Yi Zhou
https://arxiv.org/abs/2507.07387

Digital Salon: An AI and Physics-Driven Tool for 3D Hair Grooming and Simulation
We introduce Digital Salon, a comprehensive hair authoring system that supports real-time 3D hair generation, simulation, and rendering. Unlike existing methods that focus on isolated parts of 3D hair modeling and involve a heavy computation process or network training, Digital Salon offers a holistic and interactive system that lowers the technical barriers of 3D hair modeling through natural language-based interaction. The system guides users through four key stages: text-guided hair retrieva…

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
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- VR-Doh: Hands-on 3D Modeling in Virtual Reality
Zhaofeng Luo, Zhitong Cui, Shijian Luo, Mengyu Chu, Minchen Li

Object-centric 3D Motion Field for Robot Learning from Human Videos
Zhao-Heng Yin, Sherry Yang, Pieter Abbeel
https://arxiv.org/abs/2506.04227 https://

Object-centric 3D Motion Field for Robot Learning from Human Videos
Learning robot control policies from human videos is a promising direction for scaling up robot learning. However, how to extract action knowledge (or action representations) from videos for policy learning remains a key challenge. Existing action representations such as video frames, pixelflow, and pointcloud flow have inherent limitations such as modeling complexity or loss of information. In this paper, we propose to use object-centric 3D motion field to represent actions for robot learning …

This https://arxiv.org/abs/2412.04339 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction
Medical image reconstruction with pre-trained score-based generative models (SGMs) has advantages over other existing state-of-the-art deep-learned reconstruction methods, including improved resilience to different scanner setups and advanced image distribution modeling. SGM-based reconstruction has recently been applied to simulated positron emission tomography (PET) datasets, showing improved contrast recovery for out-of-distribution lesions relative to the state-of-the-art. However, existing…

ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies
Jinyan Yuan, Bangbang Yang, Keke Wang, Panwang Pan, Lin Ma, Xuehai Zhang, Xiao Liu, Zhaopeng Cui, Yuewen Ma
https://arxiv.org/abs/2506.14315

ImmerseGen: Agent-Guided Immersive World Generation with Alpha-Textured Proxies
Automatic creation of 3D scenes for immersive VR presence has been a significant research focus for decades. However, existing methods often rely on either high-poly mesh modeling with post-hoc simplification or massive 3D Gaussians, resulting in a complex pipeline or limited visual realism. In this paper, we demonstrate that such exhaustive modeling is unnecessary for achieving compelling immersive experience. We introduce ImmerseGen, a novel agent-guided framework for compact and photorealist…

Replaced article(s) found for astro-ph.SR. https://arxiv.org/list/astro-ph.SR/new
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- Modeling YSO Jets in 3D I: Highly Variable Asymmetric Magnetic Pressure-Driven Jets in the Polar ...
Yisheng Tu, Zhi-Yun Li, Zhaohuan Zhu, Chun-Yen Hsu, Xiao Hu

AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation
Zijie Wu, Chaohui Yu, Fan Wang, Xiang Bai
https://arxiv.org/abs/2506.09982

AnimateAnyMesh: A Feed-Forward 4D Foundation Model for Text-Driven Universal Mesh Animation
Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spatio-temporal distributions and the scarcity of 4D training data. In this paper, we present AnimateAnyMesh, the first feed-forward framework that enables efficient text-driven animation of arbitrary 3D meshes. Our approach leverages a novel DyMeshVAE architecture that effectively compresses and reconstructs dynamic mes…

Graphics4Science: Computer Graphics for Scientific Impacts
Peter Yichen Chen, Minghao Guo, Hanspeter Pfister, Ming Lin, William Freeman, Qixing Huang, Han-Wei Shen, Wojciech Matusik
https://arxiv.org/abs/2506.15786

Graphics4Science: Computer Graphics for Scientific Impacts
Computer graphics, often associated with films, games, and visual effects, has long been a powerful tool for addressing scientific challenges--from its origins in 3D visualization for medical imaging to its role in modern computational modeling and simulation. This course explores the deep and evolving relationship between computer graphics and science, highlighting past achievements, ongoing contributions, and open questions that remain. We show how core methods, such as geometric reasoning an…

This https://arxiv.org/abs/2501.14147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

HAMMER: Heterogeneous, Multi-Robot Semantic Gaussian Splatting
3D Gaussian Splatting offers expressive scene reconstruction, modeling a broad range of visual, geometric, and semantic information. However, efficient real-time map reconstruction with data streamed from multiple robots and devices remains a challenge. To that end, we propose HAMMER, a server-based collaborative Gaussian Splatting method that leverages widely available ROS communication infrastructure to generate 3D, metric-semantic maps from asynchronous robot data-streams with no prior knowl…

Modality Agnostic, patient-specific digital twins modeling temporally varying digestive motion
Jorge Tapias Gomez, Nishant Nadkarni, Lando S. Bosma, Jue Jiang, Ergys D. Subashi, William P. Segars, James M. Balter, Mert R Sabuncu, Neelam Tyagi, Harini Veeraraghavan
https://arxiv.org/abs/2507.01909…

Modality Agnostic, patient-specific digital twins modeling temporally varying digestive motion
Objective: Clinical implementation of deformable image registration (DIR) requires voxel-based spatial accuracy metrics such as manually identified landmarks, which are challenging to implement for highly mobile gastrointestinal (GI) organs. To address this, patient-specific digital twins (DT) modeling temporally varying motion were created to assess the accuracy of DIR methods. Approach: 21 motion phases simulating digestive GI motion as 4D sequences were generated from static 3D patient scans…

VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding
Minchao Jiang, Shunyu Jia, Jiaming Gu, Xiaoyuan Lu, Guangming Zhu, Anqi Dong, Liang Zhang
https://arxiv.org/abs/2506.22799

VoteSplat: Hough Voting Gaussian Splatting for 3D Scene Understanding
3D Gaussian Splatting (3DGS) has become horsepower in high-quality, real-time rendering for novel view synthesis of 3D scenes. However, existing methods focus primarily on geometric and appearance modeling, lacking deeper scene understanding while also incurring high training costs that complicate the originally streamlined differentiable rendering pipeline. To this end, we propose VoteSplat, a novel 3D scene understanding framework that integrates Hough voting with 3DGS. Specifically, Segment …

Complex-Valued Holographic Radiance Fields
Yicheng Zhan, Dong-Ha Shin, Seung-Hwan Baek, Kaan Ak\c{s}it
https://arxiv.org/abs/2506.08350 https://

Complex-Valued Holographic Radiance Fields
Modeling the full properties of light, including both amplitude and phase, in 3D representations is crucial for advancing physically plausible rendering, particularly in holographic displays. To support these features, we propose a novel representation that optimizes 3D scenes without relying on intensity-based intermediaries. We reformulate 3D Gaussian splatting with complex-valued Gaussian primitives, expanding support for rendering with light waves. By leveraging RGBD multi-view images, our …

This https://arxiv.org/abs/2504.01483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

GarmageNet: A Multimodal Generative Framework for Sewing Pattern Design and Generic Garment Modeling
Realistic digital garment modeling remains a labor-intensive task due to the intricate process of translating 2D sewing patterns into high-fidelity, simulation-ready 3D garments. We introduce GarmageNet, a unified generative framework that automates the creation of 2D sewing patterns, the construction of sewing relationships, and the synthesis of 3D garment initializations compatible with physics-based simulation. Central to our approach is Garmage, a novel garment representation that encodes e…

This https://arxiv.org/abs/2405.14979 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, irregular mesh topologies, noisy surfaces, and difficulties in accommodating user edits, consequently impeding t…

This https://arxiv.org/abs/2504.01483 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

GarmageNet: A Multimodal Generative Framework for Sewing Pattern Design and Generic Garment Modeling
Realistic digital garment modeling remains a labor-intensive task due to the intricate process of translating 2D sewing patterns into high-fidelity, simulation-ready 3D garments. We introduce GarmageNet, a unified generative framework that automates the creation of 2D sewing patterns, the construction of sewing relationships, and the synthesis of 3D garment initializations compatible with physics-based simulation. Central to our approach is Garmage, a novel garment representation that encodes e…

This https://arxiv.org/abs/2505.21041 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CityGo: Lightweight Urban Modeling and Rendering with Proxy Buildings and Residual Gaussians
Accurate and efficient modeling of large-scale urban scenes is critical for applications such as AR navigation, UAV based inspection, and smart city digital twins. While aerial imagery offers broad coverage and complements limitations of ground-based data, reconstructing city-scale environments from such views remains challenging due to occlusions, incomplete geometry, and high memory demands. Recent advances like 3D Gaussian Splatting (3DGS) improve scalability and visual quality but remain li…

FlexPainter: Flexible and Multi-View Consistent Texture Generation
Dongyu Yan, Leyi Wu, Jiantao Lin, Luozhou Wang, Tianshuo Xu, Zhifei Chen, Zhen Yang, Lie Xu, Shunsi Zhang, Yingcong Chen
https://arxiv.org/abs/2506.02620

FlexPainter: Flexible and Multi-View Consistent Texture Generation
Texture map production is an important part of 3D modeling and determines the rendering quality. Recently, diffusion-based methods have opened a new way for texture generation. However, restricted control flexibility and limited prompt modalities may prevent creators from producing desired results. Furthermore, inconsistencies between generated multi-view images often lead to poor texture generation quality. To address these issues, we introduce \textbf{FlexPainter}, a novel texture generation …