5 papers
Imitating What Works: Simulation-Filtered Modular Policy Learning from Human Videos
Albert J. Zhai, Kuo-Hao Zeng, Jiasen Lu +3
The ability to learn manipulation skills by watching videos of humans has the potential to unlock a new source of highly scalable data for robot learning. Here, we tackle prehensil…
SAGE: Scalable Agentic 3D Scene Generation for Embodied AI
Hongchi Xia, Xuan Li, Zhaoshuo Li +9
Real-world data collection for embodied agents remains costly and unsafe, calling for scalable, realistic, and simulator-ready 3D environments. However, existing scene-generation s…
DRAWER: Digital Reconstruction and Articulation With Environment Realism
Hongchi Xia, Entong Su, Marius Memmel +7
Creating virtual digital replicas from real-world data unlocks significant potential across domains like gaming and robotics. In this paper, we present DRAWER, a novel framework th…
IRIS: Inverse Rendering of Indoor Scenes from Low Dynamic Range Images
Chih-Hao Lin, Jia-Bin Huang, Zhengqin Li +7
Inverse rendering seeks to recover 3D geometry, surface material, and lighting from captured images, enabling advanced applications such as novel-view synthesis, relighting, and vi…
AutoVFX: Physically Realistic Video Editing from Natural Language Instructions
Hao-Yu Hsu, Zhi-Hao Lin, Albert Zhai +2
Modern visual effects (VFX) software has made it possible for skilled artists to create imagery of virtually anything. However, the creation process remains laborious, complex, and…