Tootfinder

BulletGen: Improving 4D Reconstruction with Bullet-Time Generation
Denys Rozumnyi, Jonathon Luiten, Numair Khan, Johannes Sch\"onberger, Peter Kontschieder
https://arxiv.org/abs/2506.18601

BulletGen: Improving 4D Reconstruction with Bullet-Time Generation
Transforming casually captured, monocular videos into fully immersive dynamic experiences is a highly ill-posed task, and comes with significant challenges, e.g., reconstructing unseen regions, and dealing with the ambiguity in monocular depth estimation. In this work we introduce BulletGen, an approach that takes advantage of generative models to correct errors and complete missing information in a Gaussian-based dynamic scene representation. This is done by aligning the output of a diffusion-…

New Frontiers in the Study of Magnetic Massive Stars with the Habitable Worlds Observatory
Alexandre David-Uraz (Central Michigan University, Mount Pleasant, Michigan, USA), V\'eronique Petit (University of Delaware, Newark, Delaware, USA), Coralie Neiner (LESIA, Paris Observatory, PSL University, CNRS, Sorbonne University, Paris-Cit\'e University, Meudon, France), Jean-Claude Bouret (Aix-Marseille University, CNRS, CNES, LAM, Marseille, France), Ya\"el Naz\'e (Groupe …

New Frontiers in the Study of Magnetic Massive Stars with the Habitable Worlds Observatory
High-mass stars are notable for several reasons: they are characterized by strong winds, which inject momentum and enriched material into their surroundings, and die spectacularly as supernovae, leaving behind compact remnants and heavy elements (such as those that make life on Earth possible). Despite their relative rarity, they play a disproportionate role in the evolution of the galaxies that host them, and likely also played a significant role in the early days of the Universe. A subset ($\…

On Differential Privacy for Adaptively Solving Search Problems via Sketching
Shiyuan Feng, Ying Feng, George Z. Li, Zhao Song, David P. Woodruff, Lichen Zhang
https://arxiv.org/abs/2506.05503

On Differential Privacy for Adaptively Solving Search Problems via Sketching
Recently differential privacy has been used for a number of streaming, data structure, and dynamic graph problems as a means of hiding the internal randomness of the data structure, so that multiple possibly adaptive queries can be made without sacrificing the correctness of the responses. Although these works use differential privacy to show that for some problems it is possible to tolerate $T$ queries using $\widetilde{O}(\sqrt{T})$ copies of a data structure, such results only apply to numer…

Stochastic Preconditioning for Neural Field Optimization
Selena Ling, Merlin Nimier-David, Alec Jacobson, Nicholas Sharp
https://arxiv.org/abs/2505.20473 h…

Stochastic Preconditioning for Neural Field Optimization
Neural fields are a highly effective representation across visual computing. This work observes that fitting these fields is greatly improved by incorporating spatial stochasticity during training, and that this simple technique can replace or even outperform custom-designed hierarchies and frequency space constructions. The approach is formalized as implicitly operating on a blurred version of the field, evaluated in-expectation by sampling with Gaussian-distributed offsets. Querying the blurr…