mlsanityData to 7 Oct 2026

Generative Motion Infilling From Imprecisely Timed Keyframes

arXiv 2503.01016 ↗First posted 2 Mar 2025cs.GR cs.CVcited by 4

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Abstract (arXiv)

Keyframes are a standard representation for kinematic motion specification. Recent learned motion-inbetweening methods use keyframes as a way to control generative motion models, and are trained to generate life-like motion that matches the exact poses and timings of input keyframes. However, the quality of generated motion may degrade if the timing of these constraints is not perfectly consistent with the desired motion. Unfortunately, correctly specifying keyframe timings is a tedious and challenging task in practice. Our goal is to create a system that synthesizes high-quality motion from keyframes, even if keyframes are imprecisely timed. We present a method that allows constraints to be retimed as part of the generation process. Specifically, we introduce a novel model architecture that explicitly outputs a time-warping function to correct mistimed keyframes, and spatial residuals that add pose details. We demonstrate how our method can automatically turn approximately timed keyframe constraints into diverse, realistic motions with plausible timing and detailed submovements.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 29 of its 46 references that are in the map. First posted on arXiv 2 Mar 2025.

Builds on

29 of 46 references.

References · 29

  1. 2019AMASS: Archive of Motion Capture as Surface Shapescited by 1,208
  2. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,186
  3. 2022Human Motion Diffusion Modelcited by 1,062
  4. 2022MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Modelcited by 768
  5. 2022Executing your Commands via Motion Diffusion in Latent Spacecited by 567
  6. 2022PhysDiff: Physics-Guided Human Motion Diffusion Modelcited by 365
  7. 2023Human Motion Diffusion as a Generative Priorcited by 354
  8. 2020HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimationcited by 304
  9. 2022EDGE: Editable Dance Generation From Musiccited by 284
  10. 2023OmniControl: Control Any Joint at Any Time for Human Motion Generationcited by 233
  11. 2023Guided Motion Diffusion for Controllable Human Motion Synthesiscited by 221
  12. 2022MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesiscited by 218
17 more
  1. 2021Synthesis of Compositional Animations from Textual Descriptionscited by 177
  2. 2023Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusioncited by 137
  3. 2017Text2Action: Generative Adversarial Synthesis from Language to Actioncited by 106
  4. 2023Optimizing Diffusion Noise Can Serve As Universal Motion Priorscited by 97
  5. 2022Ego-Body Pose Estimation via Ego-Head Pose Estimationcited by 93
  6. 2024Multi-Track Timeline Control for Text-Driven 3D Human Motion Generationcited by 88
  7. 2023CIRCLE: Capture In Rich Contextual Environmentscited by 79
  8. 2020Convolutional Autoencoders for Human Motion Infillingcited by 73
  9. 2024Flexible Motion In-betweening with Diffusion Modelscited by 69
  10. 2023LooseControl: Lifting ControlNet for Generalized Depth Conditioningcited by 68
  11. 2024MotionFix: Text-Driven 3D Human Motion Editingcited by 63
  12. 2018Recurrent Transition Networks for Character Locomotioncited by 52
  13. 2023Iterative Motion Editing with Natural Languagecited by 39
  14. 2023MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusioncited by 37
  15. 2023Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolationcited by 12
  16. 2023Enhanced Fine-grained Motion Diffusion for Text-driven Human Motion Synthesiscited by 10
  17. 2024Block and Detail: Scaffolding Sketch-to-Image Generationcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.