Tootfinder

CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints
Yuhong Deng, Chao Tang, Cunjun Yu, Linfeng Li, David Hsu
https://arxiv.org/abs/2507.19983 https://

CLASP: General-Purpose Clothes Manipulation with Semantic Keypoints
Clothes manipulation, such as folding or hanging, is a critical capability for home service robots. Despite recent advances, most existing methods remain limited to specific tasks and clothes types, due to the complex, high-dimensional geometry of clothes. This paper presents CLothes mAnipulation with Semantic keyPoints (CLASP), which aims at general-purpose clothes manipulation over different clothes types, T-shirts, shorts, skirts, long dresses, ... , as well as different tasks, folding, flat…

Shape-from-Template with Generalised Camera
Agniva Sengupta, Stefan Zachow
https://arxiv.org/abs/2508.13791 https://arxiv.org/pdf/2508.13791

Shape-from-Template with Generalised Camera
This article presents a new method for non-rigidly registering a 3D shape to 2D keypoints observed by a constellation of multiple cameras. Non-rigid registration of a 3D shape to observed 2D keypoints, i.e., Shape-from-Template (SfT), has been widely studied using single images, but SfT with information from multiple-cameras jointly opens new directions for extending the scope of known use-cases such as 3D shape registration in medical imaging and registration from hand-held cameras, to name a …

Deep Sensorimotor Control by Imitating Predictive Models of Human Motion
Himanshu Gaurav Singh, Pieter Abbeel, Jitendra Malik, Antonio Loquercio
https://arxiv.org/abs/2508.18691

Deep Sensorimotor Control by Imitating Predictive Models of Human Motion
As the embodiment gap between a robot and a human narrows, new opportunities arise to leverage datasets of humans interacting with their surroundings for robot learning. We propose a novel technique for training sensorimotor policies with reinforcement learning by imitating predictive models of human motions. Our key insight is that the motion of keypoints on human-inspired robot end-effectors closely mirrors the motion of corresponding human body keypoints. This enables us to use a model train…

Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
Tiago Assis, Ines P. Machado, Benjamin Zwick, Nuno C. Garcia, Reuben Dorent
https://arxiv.org/abs/2508.13762

Deep Biomechanically-Guided Interpolation for Keypoint-Based Brain Shift Registration
Accurate compensation of brain shift is critical for maintaining the reliability of neuronavigation during neurosurgery. While keypoint-based registration methods offer robustness to large deformations and topological changes, they typically rely on simple geometric interpolators that ignore tissue biomechanics to create dense displacement fields. In this work, we propose a novel deep learning framework that estimates dense, physically plausible brain deformations from sparse matched keypoints.…

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Subhajit Maity, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song
https://arxiv.org/abs/2507.07994

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a gr…

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
Bingran Chen, Baorun Li, Jian Yang, Yong Liu, Guangyao Zhai
https://arxiv.org/abs/2507.14820

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor noise, or infer 3D information from RGB images, which introduces expensive annotation requirements and discretization issues. Recent methods mitigate some challenges by retaining a 2D representation to estimate grasp keypoints and applying Perspective-n-Point (…

ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Yunchu Zhang, Shubham Mittal, Zhengyu Zhang, Liyiming Ke, Siddhartha Srinivasa, Abhishek Gupta
https://arxiv.org/abs/2506.13867

ATK: Automatic Task-driven Keypoint Selection for Robust Policy Learning
Visuomotor policies often suffer from perceptual challenges, where visual differences between training and evaluation environments degrade policy performance. Policies relying on state estimations, like 6D pose, require task-specific tracking and are difficult to scale, while raw sensor-based policies may lack robustness to small visual disturbances.In this work, we leverage 2D keypoints - spatially consistent features in the image frame - as a flexible state representation for robust policy le…

GECO: Geometrically Consistent Embedding with Lightspeed Inference
Regine Hartwig, Dominik Muhle, Riccardo Marin, Daniel Cremers
https://arxiv.org/abs/2508.00746 https://…

GECO: Geometrically Consistent Embedding with Lightspeed Inference
Recent advances in feature learning have shown that self-supervised vision foundation models can capture semantic correspondences but often lack awareness of underlying 3D geometry. GECO addresses this gap by producing geometrically coherent features that semantically distinguish parts based on geometry (e.g., left/right eyes, front/back legs). We propose a training framework based on optimal transport, enabling supervision beyond keypoints, even under occlusions and disocclusions. With a light…