183 citations · 337 across the 14 of their papers we have counts for
5 papers · 1 filter
Spatial Transform Decoupling for Oriented Object Detection
Hongtian Yu, Yunjie Tian, Qixiang Ye +1
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks. However, their potential in rotation-sensitive scenarios has not been fully explored, and this…
Multi-Object Manipulation via Object-Centric Neural Scattering Functions
Stephen Tian, Yancheng Cai, Hong-Xing Yu +5
Learned visual dynamics models have proven effective for robotic manipulation tasks. Yet, it remains unclear how best to represent scenes involving multi-object interactions. Curre…
Learning Object-Centric Neural Scattering Functions for Free-Viewpoint Relighting and Scene Composition
Hong-Xing Yu, Michelle Guo, Alireza Fathi +5
Photorealistic object appearance modeling from 2D images is a constant topic in vision and graphics. While neural implicit methods (such as Neural Radiance Fields) have shown high-…
Learning Vortex Dynamics for Fluid Inference and Prediction
Yitong Deng, Hong-Xing Yu, Jiajun Wu +1
We propose a novel differentiable vortex particle (DVP) method to infer and predict fluid dynamics from a single video. Lying at its core is a particle-based latent space to encaps…
Accidental Light Probes
Hong-Xing Yu, Samir Agarwala, Charles Herrmann +4
Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes a…