{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/lasr-learning-articulated-shape","title":"LASR: Learning Articulated Shape Reconstruction from a Monocular Video","arxiv_id":"2105.02976","date":"2021-05-06","proceeding":"CVPR 2021 1","authors":["Gengshan Yang","Deqing Sun","Varun Jampani","Daniel Vlasic","Forrester Cole","Huiwen Chang","Deva Ramanan","William T. Freeman","Ce Liu"],"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. 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