# Generative Motion Infilling From Imprecisely Timed Keyframes

arXiv: https://arxiv.org/abs/2503.01016 · HTML: https://mlsanity.com/paper/2503.01016/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 46 references that are in the map.
- First posted on arXiv 2 Mar 2025.

## Builds on (most cited first, 29 of 29)

- [AMASS: Archive of Motion Capture as Surface Shapes](https://mlsanity.com/paper/1904.03278/) (2019)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model](https://mlsanity.com/paper/2208.15001/) (2022)
- [Executing your Commands via Motion Diffusion in Latent Space](https://mlsanity.com/paper/2212.04048/) (2022)
- [PhysDiff: Physics-Guided Human Motion Diffusion Model](https://mlsanity.com/paper/2212.02500/) (2022)
- [Human Motion Diffusion as a Generative Prior](https://mlsanity.com/paper/2303.01418/) (2023)
- [HybrIK: A Hybrid Analytical-Neural Inverse Kinematics Solution for 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2011.14672/) (2020)
- [EDGE: Editable Dance Generation From Music](https://mlsanity.com/paper/2211.10658/) (2022)
- [OmniControl: Control Any Joint at Any Time for Human Motion Generation](https://mlsanity.com/paper/2310.08580/) (2023)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [MoFusion: A Framework for Denoising-Diffusion-based Motion Synthesis](https://mlsanity.com/paper/2212.04495/) (2022)
- [Synthesis of Compositional Animations from Textual Descriptions](https://mlsanity.com/paper/2103.14675/) (2021)
- [Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion](https://mlsanity.com/paper/2304.01893/) (2023)
- [Text2Action: Generative Adversarial Synthesis from Language to Action](https://mlsanity.com/paper/1710.05298/) (2017)
- [Optimizing Diffusion Noise Can Serve As Universal Motion Priors](https://mlsanity.com/paper/2312.11994/) (2023)
- [Ego-Body Pose Estimation via Ego-Head Pose Estimation](https://mlsanity.com/paper/2212.04636/) (2022)
- [Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation](https://mlsanity.com/paper/2401.08559/) (2024)
- [CIRCLE: Capture In Rich Contextual Environments](https://mlsanity.com/paper/2303.17912/) (2023)
- [Convolutional Autoencoders for Human Motion Infilling](https://mlsanity.com/paper/2010.11531/) (2020)
- [Flexible Motion In-betweening with Diffusion Models](https://mlsanity.com/paper/2405.11126/) (2024)
- [LooseControl: Lifting ControlNet for Generalized Depth Conditioning](https://mlsanity.com/paper/2312.03079/) (2023)
- [MotionFix: Text-Driven 3D Human Motion Editing](https://mlsanity.com/paper/2408.00712/) (2024)
- [Recurrent Transition Networks for Character Locomotion](https://mlsanity.com/paper/1810.02363/) (2018)
- [Iterative Motion Editing with Natural Language](https://mlsanity.com/paper/2312.11538/) (2023)
- [MAS: Multi-view Ancestral Sampling for 3D motion generation using 2D diffusion](https://mlsanity.com/paper/2310.14729/) (2023)
- [Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation](https://mlsanity.com/paper/2303.14926/) (2023)
- [Enhanced Fine-grained Motion Diffusion for Text-driven Human Motion Synthesis](https://mlsanity.com/paper/2305.13773/) (2023)
- [Block and Detail: Scaffolding Sketch-to-Image Generation](https://mlsanity.com/paper/2402.18116/) (2024)

## Built on by (most cited first, 4 of 4)

- [Skinned Motion Retargeting with Spatially Adaptive Interaction Guidance](https://mlsanity.com/paper/2605.19355/) (2026)
- [Generating Detailed Character Motion from Blocking Poses](https://mlsanity.com/paper/2509.16064/) (2025)
- [Motion In-Betweening for Densely Interacting Characters](https://mlsanity.com/paper/2510.00314/) (2025)
- [Adaptive Interpolation-Synthesis for Motion In-Betweening on Keyframe-Based Animation](https://mlsanity.com/paper/2605.02742/) (2026)

## 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.