Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations

• URL: http://arxiv.org/abs/2608.01628v2
• Date: Tue, 04 Aug 2026 03:38:05 GMT
• Title: Motion Beyond Morphology: Bootstrapping Cross-Category Motion Transfer from Abstract Motion Representations
• Abstract summary: Video motion transfer aims to animate a target object using dynamics from a reference video.<n>Existing formulations largely rely on fixed structural correspondence.<n>We introduce Motion Beyond Morphology, a perspective that seeks to transfer motion beyond fixed structural correspondence.
• Score: 32.70813770106954
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video motion transfer aims to animate a target object using dynamics from a reference video. Existing formulations largely rely on fixed structural correspondence, which becomes ill-defined when reference and target objects differ substantially in morphology, articulation, or deformation mechanisms. We introduce Motion Beyond Morphology, a perspective that seeks to transfer motion beyond fixed structural correspondence, by preserving dynamics that remain meaningful across different target morphologies. To realize this, we propose a two-stage framework. Stage~I learns complementary multi-granularity abstract motion views and uses them to bootstrap cross-category video pairs that preserve transferable dynamics across diverse morphologies. Stage~II internalizes this supervision into direct reference-video-conditioned generation, removing the need for explicit motion extraction at inference. We further introduce OpenVMT-Dataset and OpenVMT-Bench for training and evaluating image- and text-conditioned motion transfer across Same, Near, and Far category gaps. Extensive experiments demonstrate state-of-the-art motion fidelity and target preservation. Project page: https://miniz233.github.io/MotionBeyondMorphology/

Related papers

• Training-free Motion Factorization for Compositional Video Generation [57.819757612370374]
  We propose a motion factorization framework that decomposes complex motion into three primary categories: motionlessness, rigid motion, and non-rigid motion.<n>Our framework achieves impressive performance in motion synthesis on real-world benchmarks.
  arXiv  Detail & Related papers  (2026-03-10T02:27:48Z)
• MotionAdapter: Video Motion Transfer via Content-Aware Attention Customization [73.07309070257162]
  MotionAdapter is a content-aware motion transfer framework that enables robust and semantically aligned motion transfer.<n>Our key insight is that effective motion transfer requires explicit disentanglement of motion from appearance.<n> MotionAdapter naturally supports complex motion transfer and motion editing tasks such as zooming.
  arXiv  Detail & Related papers  (2026-01-05T10:01:27Z)
• DisMo: Disentangled Motion Representations for Open-World Motion Transfer [21.557843791867906]
  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.
  arXiv  Detail & Related papers  (2025-11-28T18:25:54Z)
• MotionShot: Adaptive Motion Transfer across Arbitrary Objects for Text-to-Video Generation [23.051430600796277]
  MotionShot is a framework for parsing reference-target correspondences in a fine-grained manner.<n>It can coherently transfer motion across objects, even in the presence of significant appearance and structure disparities.
  arXiv  Detail & Related papers  (2025-07-22T07:51:05Z)
• 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)
• Segment Any Motion in Videos [80.72424676419755]
  We propose a novel approach for moving object segmentation that combines long-range trajectory motion cues with DINO-based semantic features.<n>Our model employs Spatio-Temporal Trajectory Attention and Motion-Semantic Decoupled Embedding to prioritize motion while integrating semantic support.
  arXiv  Detail & Related papers  (2025-03-28T09:34:11Z)
• Spectral Motion Alignment for Video Motion Transfer using Diffusion Models [54.32923808964701]
  Spectral Motion Alignment (SMA) is a framework that refines and aligns motion vectors using Fourier and wavelet transforms.<n> SMA learns motion patterns by incorporating frequency-domain regularization, facilitating the learning of whole-frame global motion dynamics.<n>Extensive experiments demonstrate SMA's efficacy in improving motion transfer while maintaining computational efficiency and compatibility across various video customization frameworks.
  arXiv  Detail & Related papers  (2024-03-22T14:47:18Z)
• 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)

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.