14 papers
Everything at Every Scale: Scale-Invariant Diffusion with Continuous Super-Resolution
Zixin Jessie Chen, Zhuo Chen, Archer Wang +4
Creating images from noise is image generation; reconstructing fine details from coarse inputs is super-resolution. Despite their practical differences, both can be understood as r…
Rodrigues Network for Learning Robot Actions
Jialiang Zhang, Haoran Geng, Yang You +4
Understanding and predicting articulated actions is important in robot learning. However, common architectures such as MLPs and Transformers lack inductive biases that reflect the…
DeepShapeMatchingKit: Accelerated Functional Map Solver and Shape Matching Pipelines Revisited
Yizheng Xie, Lennart Bastian, Congyue Deng +3
Deep functional maps, leveraging learned feature extractors and spectral correspondence solvers, are fundamental to non-rigid 3D shape matching. Based on an analysis of open-source…
RINO: Rotation-Invariant Non-Rigid Correspondences
Maolin Gao, Shao Jie Hu-Chen, Congyue Deng +3
Dense 3D shape correspondence remains a central challenge in computer vision and graphics as many deep learning approaches still rely on intermediate geometric features or handcraf…
Denoising Hamiltonian Network for Physical Reasoning
Congyue Deng, Brandon Y. Feng, Cecilia Garraffo +5
Machine learning frameworks for physical problems must capture and enforce physical constraints that preserve the structure of dynamical systems. Many existing approaches achieve t…
Multiview Equivariance Improves 3D Correspondence Understanding with Minimal Feature Finetuning
Yang You, Yixin Li, Congyue Deng +2
Vision foundation models, particularly the ViT family, have revolutionized image understanding by providing rich semantic features. However, despite their success in 2D comprehensi…