DisMo: Disentangled Motion Representations for Open-World Motion Transfer

• URL: http://arxiv.org/abs/2511.23428v1
• Date: Fri, 28 Nov 2025 18:25:54 GMT
• Title: DisMo: Disentangled Motion Representations for Open-World Motion Transfer
• Authors: Thomas Ressler-Antal, Frank Fundel, Malek Ben Alaya, Stefan Andreas Baumann, Felix Krause, Ming Gui, Björn Ommer,
• Abstract summary: DisMo is a novel paradigm for learning abstract motion representations directly from raw video data.<n>Our representation is generic and independent of static information such as appearance, object identity, or pose.<n>We show that the learned representations are well-suited for downstream motion understanding tasks.
• Score: 21.557843791867906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in text-to-video (T2V) and image-to-video (I2V) models, have enabled the creation of visually compelling and dynamic videos from simple textual descriptions or initial frames. However, these models often fail to provide an explicit representation of motion separate from content, limiting their applicability for content creators. To address this gap, we propose DisMo, a novel paradigm for learning abstract motion representations directly from raw video data via an image-space reconstruction objective. Our representation is generic and independent of static information such as appearance, object identity, or pose. This enables open-world motion transfer, allowing motion to be transferred across semantically unrelated entities without requiring object correspondences, even between vastly different categories. Unlike prior methods, which trade off motion fidelity and prompt adherence, are overfitting to source structure or drifting from the described action, our approach disentangles motion semantics from appearance, enabling accurate transfer and faithful conditioning. Furthermore, our motion representation can be combined with any existing video generator via lightweight adapters, allowing us to effortlessly benefit from future advancements in video models. We demonstrate the effectiveness of our method through a diverse set of motion transfer tasks. Finally, we show that the learned representations are well-suited for downstream motion understanding tasks, consistently outperforming state-of-the-art video representation models such as V-JEPA in zero-shot action classification on benchmarks including Something-Something v2 and Jester. Project page: https://compvis.github.io/DisMo

Related papers

• IM-Animation: An Implicit Motion Representation for Identity-decoupled Character Animation [58.297199313494]
  Implicit methods capture motion semantics directly from driving video, but suffer from identity leakage and entanglement between motion and appearance.<n>We propose a novel implicit motion representation that compresses per-frame motion into compact 1D motion tokens.<n>Our methodology employs a three-stage training strategy to enhance the training efficiency and ensure high fidelity.
  arXiv  Detail & Related papers  (2026-02-07T11:17:20Z)
• SynMotion: Semantic-Visual Adaptation for Motion Customized Video Generation [56.90807453045657]
  SynMotion is a motion-customized video generation model that jointly leverages semantic guidance and visual adaptation.<n>At the semantic level, we introduce the dual-em semantic comprehension mechanism which disentangles subject and motion representations.<n>At the visual level, we integrate efficient motion adapters into a pre-trained video generation model to enhance motion fidelity and temporal coherence.
  arXiv  Detail & Related papers  (2025-06-30T10:09:32Z)
• Separate Motion from Appearance: Customizing Motion via Customizing Text-to-Video Diffusion Models [31.84209035471775]
  Motion customization aims to adapt the diffusion model (DM) to generate videos with the motion specified by a set of video clips with the same motion concept.<n>This paper proposes two novel strategies to enhance motion-appearance separation, including temporal attention purification (TAP) and appearance highway (AH)
  arXiv  Detail & Related papers  (2025-01-28T05:40:20Z)
• Motion Prompting: Controlling Video Generation with Motion Trajectories [57.049252242807874]
  We train a video generation model conditioned on sparse or dense video trajectories.<n>We translate high-level user requests into detailed, semi-dense motion prompts.<n>We demonstrate our approach through various applications, including camera and object motion control, "interacting" with an image, motion transfer, and image editing.
  arXiv  Detail & Related papers  (2024-12-03T18:59:56Z)
• MoTrans: Customized Motion Transfer with Text-driven Video Diffusion Models [59.10171699717122]
  MoTrans is a customized motion transfer method enabling video generation of similar motion in new context.<n> multimodal representations from recaptioned prompt and video frames promote the modeling of appearance.<n>Our method effectively learns specific motion pattern from singular or multiple reference videos.
  arXiv  Detail & Related papers  (2024-12-02T10:07:59Z)
• Reenact Anything: Semantic Video Motion Transfer Using Motion-Textual Inversion [9.134743677331517]
  We propose a pre-trained image-to-video model to disentangle appearance from motion.<n>Our method, called motion-textual inversion, leverages our observation that image-to-video models extract appearance mainly from the (latent) image input.<n>By operating on an inflated motion-text embedding containing multiple text/image embedding tokens per frame, we achieve a high temporal motion granularity.<n>Our approach does not require spatial alignment between the motion reference video and target image, generalizes across various domains, and can be applied to various tasks such as full-body and face reenactment.
  arXiv  Detail & Related papers  (2024-08-01T10:55:20Z)
• Animate Your Motion: Turning Still Images into Dynamic Videos [58.63109848837741]
  We introduce Scene and Motion Conditional Diffusion (SMCD), a novel methodology for managing multimodal inputs. SMCD incorporates a recognized motion conditioning module and investigates various approaches to integrate scene conditions. Our design significantly enhances video quality, motion precision, and semantic coherence.
  arXiv  Detail & Related papers  (2024-03-15T10:36:24Z)
• VMC: Video Motion Customization using Temporal Attention Adaption for Text-to-Video Diffusion Models [58.93124686141781]
  Video Motion Customization (VMC) is a novel one-shot tuning approach crafted to adapt temporal attention layers within video diffusion models. Our approach introduces a novel motion distillation objective using residual vectors between consecutive frames as a motion reference. We validate our method against state-of-the-art video generative models across diverse real-world motions and contexts.
  arXiv  Detail & Related papers  (2023-12-01T06:50:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.