52 citations · 70 across the 17 of their papers we have counts for
4 papers · 1 filter
BANMo: Building Animatable 3D Neural Models from Many Casual Videos
Gengshan Yang, Minh Vo, Natalia Neverova +3
Prior work for articulated 3D shape reconstruction often relies on specialized sensors (e.g., synchronized multi-camera systems), or pre-built 3D deformable models (e.g., SMAL or S…
NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild
Jason Y. Zhang, Gengshan Yang, Shubham Tulsiani +1
Recent history has seen a tremendous growth of work exploring implicit representations of geometry and radiance, popularized through Neural Radiance Fields (NeRF). Such works are f…
LASR: Learning Articulated Shape Reconstruction from a Monocular Video
Gengshan Yang, Deqing Sun, Varun Jampani +6
Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structur…
Learning to Segment Rigid Motions from Two Frames
Gengshan Yang, Deva Ramanan
Appearance-based detectors achieve remarkable performance on common scenes, but tend to fail for scenarios lack of training data. Geometric motion segmentation algorithms, however,…