3D Shape Reconstruction from Videos

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (5 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Reconstructing Animatable Categories from Videos
  10 May 2023 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified
  Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories.
• DeciWatch: A Simple Baseline for 10x Efficient 2D and 3D Pose Estimation
  16 Mar 2022 1 repository listed Syntology ran 1 of 8 samples · 7 unverified
  This paper proposes a simple baseline framework for video-based 2D/3D human pose estimation that can achieve 10 times efficiency improvement over existing works without any performance degradation, named DeciWatch.
• BANMo: Building Animatable 3D Neural Models from Many Casual Videos
  23 Dec 2021 1 repository listed
  Our key insight is to merge three schools of thought; (1) classic deformable shape models that make use of articulated bones and blend skinning, (2) volumetric neural radiance fields (NeRFs) that are amenable to…
• ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction
  1 Dec 2021 1 repository listed
  The surface embeddings are implemented as coordinate-based MLPs that are fit to each video via consistency and contrastive reconstruction losses.
• LASR: Learning Articulated Shape Reconstruction from a Monocular Video
  6 May 2021 1 repository listed Syntology ran 0 of 7 samples · 7 unverified
  Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.