3D Human Dynamics

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

1 subtask in the archive's task tree.

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.

• InterDiff: Generating 3D Human-Object Interactions with Physics-Informed Diffusion
  31 Aug 2023 1 repository listed Syntology ran 6 of 6 samples · 0 unverified
  This paper addresses a novel task of anticipating 3D human-object interactions (HOIs).
• AutoAvatar: Autoregressive Neural Fields for Dynamic Avatar Modeling
  25 Mar 2022 1 repository listed
  In this work, for the first time, we enable autoregressive modeling of implicit avatars.
• Dynamical Variational Autoencoders: A Comprehensive Review
  28 Aug 2020 1 repository listed
  Recently, a series of papers have presented different extensions of the VAE to process sequential data, which model not only the latent space but also the temporal dependencies within a sequence of data vectors and…
• Predicting 3D Human Dynamics from Video
  13 Aug 2019 1 repository listed Syntology ran 0 of 17 samples · 17 unverified
  In this work, we present perhaps the first approach for predicting a future 3D mesh model sequence of a person from past video input.
• Learning 3D Human Dynamics from Video
  4 Dec 2018 1 repository listed Syntology ran 0 of 7 samples · 7 unverified
  We present a framework that can similarly learn a representation of 3D dynamics of humans from video via a simple but effective temporal encoding of image features.

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.