Paper Info
Title
LASR: Learning Articulated Shape Reconstruction from a Monocular Video
Abstract
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 structures from RGB inputs, due to its under-constrained nature. While template-based approaches, such as parametric shape models, have achieved great success in modeling the "closed world" of known object categories, they cannot well handle the "open-world" of novel object categories or outlier shapes. In this work, we introduce a template-free approach to learn 3D shapes from a single video. It adopts an analysis-by-synthesis strategy that forward-renders object silhouette, optical flow, and pixel values to compare with video observations, which generates gradients to adjust the camera, shape and motion parameters. Without using a category-specific shape template, our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes. Our code is available at lasr-google.github.io.
Year
DOI
Venue
2021
10.1109/CVPR46437.2021.01572
2021 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, CVPR 2021
DocType
ISSN
Citations 
Conference
1063-6919
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Gengshan Yang1153.34
Deqing Sun2106144.84
Varun Jampani318419.44
Daniel Vlasic411.03
Forrester Cole542020.41
Huiwen Chang6264.73
deva ramanan710678566.72
William T. Freeman8173821968.76
Ce Liu93347188.04