Author Identifier (ORCID)
Abstract
Animation production workflows centered around motion capture techniques require animators to edit motions based on a set of keyframes. However, most existing keyframe selection methods are optimization-based, which suffer from the issues of flexibility and efficiency. In this paper, a novel deep reinforcement learning method with dual agents are proposed for unsupervised keyframe selection. First, an S-Agent and an R-Agent evaluate the actions of selection and refinement, respectively. A deep spatio-temporal network, namely graph keyframe evaluation network (GKEN), is proposed for the agents. Then, an animation specified reward is devised based on reconstruction, which fulfills three important properties of the animation workflow: incremental reward, order insensitivity and non-diminishing returns. During the inference, it is no longer necessary to compute the reconstruction, which significantly decreases the run-time latency. Experiments on the CMU MoCap dataset demonstrate the efficiency of the proposed method without clearly compromising the effectiveness compared with the state-of-the-art methods.
Keywords
Animation, deep learning, keyframe selection, motion capture, reinforcement learning
Document Type
Journal Article
Date of Publication
11-1-2026
Volume
179
Publication Title
Pattern Recognition
Publisher
Elsevier
School
School of Science
Funders
This study was partially supported by ARC Discovery Project [DP210102674] and ECU Early-Mid Career Researcher (EMCR) Grant Scheme 2026 .
Grant Number
ARC Number : DP210102674
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Hu, K., Wang, X., Mo, C. A., Ma, M., Mei, S., Chen, Z., & Wang, Z. (2026). Keyframe selection from motion capture data with dual-agent reinforcement learning. Pattern Recognition, 179, 113775. https://doi.org/10.1016/j.patcog.2026.113775