9 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…
Thinking in Blender: Staged Executable Inverse Graphics with Vision-Language Models
Guangzhao He, Rundong Luo, Wei-Chiu Ma +1
Inverse graphics is a longstanding and highly underconstrained problem that seeks to reconstruct images as editable 3D scenes which can be rendered, relit, and manipulated. In this…
Seeing Fast and Slow: Learning the Flow of Time in Videos
Yen-Siang Wu, Rundong Luo, Jingsen Zhu +6
How can we tell whether a video has been sped up or slowed down? How can we generate videos at different speeds? Although videos have been central to modern computer vision researc…
Beyond the Frame: Generating 360 Panoramic Videos from Perspective Videos
Rundong Luo, Matthew Wallingford, Ali Farhadi +2
360° videos have emerged as a promising medium to represent our dynamic visual world. Compared to the "tunnel vision" of standard cameras, their borderless field of view offers a…
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…
Eval3D: Interpretable and Fine-grained Evaluation for 3D Generation
Shivam Duggal, Yushi Hu, Oscar Michel +7
Despite the unprecedented progress in the field of 3D generation, current systems still often fail to produce high-quality 3D assets that are visually appealing and geometrically a…