Class-agnostic Reconstruction of Dynamic Objects from VideosDownload PDF

May 21, 2021 (edited Nov 09, 2021)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: Dynamic reconstruction, implicit network, 3D vision
  • TL;DR: We introduce REDO, a class-agnostic framework to reconstruct the dynamic objects from videos
  • Abstract: We introduce REDO, a class-agnostic framework to REconstruct the Dynamic Objects from RGBD or calibrated videos. Compared to prior work, our problem setting is more realistic yet more challenging for three reasons: 1) due to occlusion or camera settings an object of interest may never be entirely visible, but we aim to reconstruct the complete shape; 2) we aim to handle different object dynamics including rigid motion, non-rigid motion, and articulation; 3) we aim to reconstruct different categories of objects with one unified framework. To address these challenges, we develop two novel modules. First, we introduce a canonical 4D implicit function which is pixel-aligned with aggregated temporal visual cues. Second, we develop a 4D transformation module which captures object dynamics to support temporal propagation and aggregation. We study the efficacy of REDO in extensive experiments on synthetic RGBD video datasets SAIL-VOS 3D and DeformingThings4D++, and on real-world video data 3DPW. We find REDO outperforms state-of-the-art dynamic reconstruction methods by a margin. In ablation studies we validate each developed component.
  • Supplementary Material: pdf
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  • Code: https://jason718.github.io/redo
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