Motion Disentanglement

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

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (12 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.

• ConMo: Controllable Motion Disentanglement and Recomposition for Zero-Shot Motion Transfer
  3 Apr 2025 1 repository listed Syntology ran 1 of 2 samples · 1 unverified · 2 pointer-only (licence)
  The development of Text-to-Video (T2V) generation has made motion transfer possible, enabling the control of video motion based on existing footage.
• MotionCrafter: One-Shot Motion Customization of Diffusion Models
  8 Dec 2023 1 repository listed
  The essence of a video lies in its dynamic motions, including character actions, object movements, and camera movements.
• Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth
  29 Mar 2022 1 repository listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced…
• Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
  28 Feb 2022 1 repository listed
  To that end, we propose to learn exercise-oriented image and video representations from unlabeled samples such that a small dataset annotated by experts suffices for supervised error detection.
• Hamiltonian latent operators for content and motion disentanglement in image sequences
  2 Dec 2021 1 repository listed Syntology ran 11 of 17 samples · 6 unverified · 17 pointer-only (licence)
  We introduce \textit{HALO} -- a deep generative model utilising HAmiltonian Latent Operators to reliably disentangle content and motion information in image sequences.
• Video Reenactment as Inductive Bias for Content-Motion Disentanglement
  30 Jan 2021 1 repository listed
  Experiments on video reenactment show the effectiveness of our disentanglement in the input space where our model outperforms the baselines in reconstruction quality and motion alignment.

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.