mlsanityData to 7 Oct 2026

Decoupling Contact for Fine-Grained Motion Style Transfer

arXiv 2409.05387 ↗First posted 9 Sep 2024cs.CV cs.GRcited by 4

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Abstract (arXiv)

Motion style transfer changes the style of a motion while retaining its content and is useful in computer animations and games. Contact is an essential component of motion style transfer that should be controlled explicitly in order to express the style vividly while enhancing motion naturalness and quality. However, it is unknown how to decouple and control contact to achieve fine-grained control in motion style transfer. In this paper, we present a novel style transfer method for fine-grained control over contacts while achieving both motion naturalness and spatial-temporal variations of style. Based on our empirical evidence, we propose controlling contact indirectly through the hip velocity, which can be further decomposed into the trajectory and contact timing, respectively. To this end, we propose a new model that explicitly models the correlations between motions and trajectory/contact timing/style, allowing us to decouple and control each separately. Our approach is built around a motion manifold, where hip controls can be easily integrated into a Transformer-based decoder. It is versatile in that it can generate motions directly as well as be used as post-processing for existing methods to improve quality and contact controllability. In addition, we propose a new metric that measures a correlation pattern of motions based on our empirical evidence, aligning well with human perception in terms of motion naturalness. Based on extensive evaluation, our method outperforms existing methods in terms of style expressivity and motion quality.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 61 references that are in the map. First posted on arXiv 9 Sep 2024.

Builds on

20 of 61 references.

References · 20

  1. 2022Human Motion Diffusion Modelcited by 1,062
  2. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 567
  3. 2023MotionGPT: Human Motion as a Foreign Languagecited by 521
  4. 2021Action-Conditioned 3D Human Motion Synthesis with Transformer VAEcited by 497
  5. 2022TEMOS: Generating diverse human motions from textual descriptionscited by 486
  6. 2022Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Modelscited by 188
  7. 2021Character Controllers Using Motion VAEscited by 182
  8. 2023GestureDiffuCLIP: Gesture Diffusion Model with CLIP Latentscited by 156
  9. 2017Text2Action: Generative Adversarial Synthesis from Language to Actioncited by 106
  10. 2020Unpaired Motion Style Transfer from Video to Animationcited by 94
  11. 2022ZeroEGGS: Zero-shot Example-based Gesture Generation from Speechcited by 83
  12. 2018Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Datacited by 76
8 more
  1. 2022Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phasescited by 71
  2. 2022Motion Puzzle: Arbitrary Motion Style Transfer by Body Partcited by 64
  3. 2018Action-Agnostic Human Pose Forecastingcited by 56
  4. 2019Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modelingcited by 32
  5. 2022Real-time Controllable Motion Transition for Characterscited by 29
  6. 2023RSMT: Real-time Stylized Motion Transition for Characterscited by 19
  7. 2020Dynamic Future Net: Diversified Human Motion Generationcited by 14
  8. 2023MOCHA: Real-Time Motion Characterization via Context Matchingcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.