research-article

Motion In-Betweening via Two-Stage Transformers

Authors:

Kun ZhouAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 6

Article No.: 184, Pages 1 - 16

https://doi.org/10.1145/3550454.3555454

Published: 30 November 2022 Publication History

Abstract

We present a deep learning-based framework to synthesize motion in-betweening in a two-stage manner. Given some context frames and a target frame, the system can generate plausible transitions with variable lengths in a non-autoregressive fashion. The framework consists of two Transformer Encoder-based networks operating in two stages: in the first stage a Context Transformer is designed to generate rough transitions based on the context and in the second stage a Detail Transformer is employed to refine motion details. Compared to existing Transformer-based methods which either use a complete Transformer Encoder-Decoder architecture or additional 1D convolutions to generate motion transitions, our framework achieves superior performance with less trainable parameters by only leveraging the Transformer Encoder and masked self-attention mechanism. To enhance the generalization of our transformer-based framework, we further introduce Keyframe Positional Encoding and Learned Relative Positional Encoding to make our method robust in synthesizing longer transitions exceeding the maximum transition length during training. Our framework is also artist-friendly by supporting full and partial pose constraints within the transition, giving artists fine control over the synthesized results. We benchmark our framework on the LAFAN1 dataset, and experiments show that our method outperforms the current state-of-the-art methods at a large margin (an average of 16% for normal-length sequences and 55% for excessive-length sequences). Our method trains faster than the RNN-based method and achieves a four-time speedup during inference. We implement our framework into a production-ready tool inside an animation authoring software and conduct a pilot study to validate the practical value of our method.

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Cited By

Chai ZQin H(2025)Dynamic Motion Transition: A Hybrid Data-Driven and Model-Driven Method for Human Pose TransitionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337242131:3(1848-1861)Online publication date: Mar-2025
https://doi.org/10.1109/TVCG.2024.3372421
Ren TYu JGuo SMa YOuyang YZeng ZZhang YQin Y(2025)Diverse Motion In-Betweening From Sparse Keyframes With Dual Posture StitchingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336345731:2(1402-1413)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TVCG.2024.3363457
Park GHwang JKwon T(2025)Sample-efficient reference-free control strategy for multi-legged locomotionComputers & Graphics10.1016/j.cag.2024.104141126(104141)Online publication date: Feb-2025
https://doi.org/10.1016/j.cag.2024.104141
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Index Terms

Motion In-Betweening via Two-Stage Transformers
1. Computing methodologies
  1. Computer graphics
    1. Animation
      1. Motion capture
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 6

December 2022

1428 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3550454

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Copyright © 2022 ACM.

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Publication History

Published: 30 November 2022

Published in TOG Volume 41, Issue 6

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Cited By

Chai ZQin H(2025)Dynamic Motion Transition: A Hybrid Data-Driven and Model-Driven Method for Human Pose TransitionsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.337242131:3(1848-1861)Online publication date: Mar-2025
https://doi.org/10.1109/TVCG.2024.3372421
Ren TYu JGuo SMa YOuyang YZeng ZZhang YQin Y(2025)Diverse Motion In-Betweening From Sparse Keyframes With Dual Posture StitchingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2024.336345731:2(1402-1413)Online publication date: 1-Feb-2025
https://dl.acm.org/doi/10.1109/TVCG.2024.3363457
Park GHwang JKwon T(2025)Sample-efficient reference-free control strategy for multi-legged locomotionComputers & Graphics10.1016/j.cag.2024.104141126(104141)Online publication date: Feb-2025
https://doi.org/10.1016/j.cag.2024.104141
Mascaró EAhn HLee DWooldridge MDy JNatarajan S(2024)A unified masked autoencoder with patchified skeletons for motion synthesisProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i6.28333(5261-5269)Online publication date: 20-Feb-2024
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Fan YWei GWang CZhuang SWang WZhou YWooldridge MDy JNatarajan S(2024)Collaborative tooth motion diffusion model in digital orthodonticsProceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v38i2.27935(1679-1687)Online publication date: 20-Feb-2024
https://dl.acm.org/doi/10.1609/aaai.v38i2.27935
Agrawal DBuhmann JBorer DSumner RGuay M(2024)SKEL-Betweener: a Neural Motion Rig for Interactive Motion AuthoringACM Transactions on Graphics10.1145/368794143:6(1-11)Online publication date: 19-Dec-2024
https://dl.acm.org/doi/10.1145/3687941
Studer JAgrawal DBorer DSadat SSumner RGuay MBuhmann J(2024)Factorized Motion Diffusion for Precise and Character-Agnostic Motion InbetweeningProceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games10.1145/3677388.3696338(1-10)Online publication date: 21-Nov-2024
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Chu YYang Z(2024)Real-time Diverse Motion In-betweening with Space-time ControlProceedings of the 17th ACM SIGGRAPH Conference on Motion, Interaction, and Games10.1145/3677388.3696327(1-8)Online publication date: 21-Nov-2024
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Xu RTran VNayar SKrishnan G(2024)DanceCraft: A Music-Reactive Real-time Dance Improv SystemProceedings of the 9th International Conference on Movement and Computing10.1145/3658852.3659078(1-10)Online publication date: 30-May-2024
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Goel PWang KLiu CFatahalian K(2024)Iterative Motion Editing with Natural LanguageACM SIGGRAPH 2024 Conference Papers10.1145/3641519.3657447(1-9)Online publication date: 13-Jul-2024
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