LASR: Learning Articulated Shape Reconstruction from a Monocular Video

• URL: http://arxiv.org/abs/2105.02976v1
• Date: Thu, 6 May 2021 21:41:11 GMT
• Title: LASR: Learning Articulated Shape Reconstruction from a Monocular Video
• Authors: Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang, Deva Ramanan, William T. Freeman, Ce Liu
• Abstract summary: We introduce a template-free approach to learn 3D shapes from a single video. Our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes.
• Score: 97.92849567637819
• License: http://creativecommons.org/licenses/by/4.0/
• 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. Code will be available at lasr-google.github.io .

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