Generative Motion Infilling From Imprecisely Timed Keyframes

2025-03-02

AI Summary

This work addresses the degradation of motion tweening quality caused by imprecise keyframe timing annotations. Existing approaches assume exact keyframe timestamps, yet practical annotations often contain temporal errors. We propose a robust motion sequence generation framework that jointly models an explicit time-warping function and spatial pose residuals. Our architecture incorporates a learnable temporal mapping module to dynamically retiming keyframes, while jointly optimizing temporal coherence and fine-grained pose details. Implemented as an end-to-end trainable generative neural network, the method requires no precise temporal priors. Extensive evaluation across multiple motion datasets demonstrates strong robustness to approximately timed keyframes: generated motions exhibit natural fluidity, plausible rhythm, and rich sub-motions—significantly outperforming fixed-timing baseline methods.

Technology Category

• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Temporal Planning
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

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.

Problem

• Generates high-quality motion from imprecisely timed keyframes.
• Corrects mistimed keyframes using a time-warping function.
• Produces realistic motions with plausible timing and detailed submovements.

Innovation

• Generates motion from imprecise keyframe timings
• Introduces time-warping function for timing correction
• Adds spatial residuals for detailed pose refinement