The DOI matches its Crossref record; 68 of the 68 references it cites were found in a registry or an index.
Robust motion in-betweening
Open accessAbstract
In this work we present a novel, robust transition generation technique that can serve as a new tool for 3D animators, based on adversarial recurrent neural networks. The system synthesises high-quality motions that use temporally-sparse keyframes as animation constraints. This is reminiscent of the job of in-betweening in traditional animation pipelines, in which an animator draws motion frames between provided keyframes. We first show that a state-of-the-art motion prediction model cannot be easily converted into a robust transition generator when only adding conditioning information about future keyframes. To solve this problem, we then propose two novel additive embedding modifiers that are applied at each timestep to latent representations encoded inside the network's architecture. One modifier is a time-to-arrival embedding that allows variations of the transition length with a single model. The other is a scheduled target noise vector that allows the system to be robust to target distortions and to sample different transitions given fixed keyframes. To qualitatively evaluate our method, we present a custom MotionBuilder plugin that uses our trained model to perform in-betweening in production scenarios. To quantitatively evaluate performance on transitions and generalizations to longer time horizons, we present well-defined in-betweening benchmarks on a subset of the widely used Human3.6M dataset and on LaFAN1, a novel high quality motion capture dataset that is more appropriate for transition generation. We are releasing this new dataset along with this work, with accompanying code for reproducing our baseline results.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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Labaca-Castro, R. (2023) · Machine Learning under Malware Attack · doi:10.1007/978-3-658-40442-0_9 · Cited by 19,272
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11671 · Cited by 2,490
International Journal of Computer Vision
(2013) · International Journal of Computer Vision · doi:10.1007/11263.1573-1405 · Cited by 2,340
Generative adversarial imitation learning
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On the Convergence of Adam and Beyond
Reddi, S. J., Kale, S., & Kumar, S. (2019) · arXiv · doi:10.48550/arxiv.1904.09237 · Cited by 1,771
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Cited by
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., & Liu, Z. (2022) · arXiv · doi:10.48550/arxiv.2208.15001 · Cited by 437
Generating Human Motion from Textual Descriptions with Discrete Representations
Zhang, J., Zhang, Y., Cun, X., Zhang, Y., Zhao, H., Lu, H., Shen, X., & Ying, S. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01415 · Cited by 328
“In addition to synthesizing motion conditioning on past motion, another related topic is generating motion “in-betweening” that takes both past and future poses and fills motion between them [18,27,28,35,64].”
BackgroundLi, T., Won, J., Clegg, A., Kim, J., Rai, A., & Ha, S. (2023) · SIGGRAPH Asia 2023 Conference Papers · doi:10.1145/3610548.3618255 · Cited by 18
Learning Embodied Sound-Motion Mappings: Evaluating AI-Generated Dance Improvisation
Wallace, B., Martin, C. P., Tørresen, J., & Nymoen, K. (2021) · Creativity and Cognition · doi:10.1145/3450741.3465245 · Cited by 13
Motion2Motion: Cross-topology Motion Transfer with Sparse Correspondence
Chen, L.-H., Zhang, Y., Yin, Z., Dou, Z., Chen, X., Wang, J., Komura, T., & Zhang, L. (2025) · Proceedings of the SIGGRAPH Asia 2025 Conference Papers · doi:10.1145/3757377.3763811 · Cited by 3
Metadata
- Authors
- Harvey, F. G., Yurick, M., Nowrouzezahrai, D., & Pal, C.
- Venue
- ACM Transactions on Graphics
- Published
- 2020-08-12
- Volume
- 39
- Issue
- 4
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W3048474702
- Cited by
- 266
- References
- 68
- FWCI
- 16.39
- Type
- journal article
- Language
- en
- Open access
- green
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