The DOI matches its Crossref record; 90 of the 94 references it cites were found in a registry or an index.
AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Open accessAbstract
We present AIST++, a new multi-modal dataset of 3D dance motion and music, along with FACT, a Full-Attention Cross-modal Transformer network for generating 3D dance motion conditioned on music. The proposed AIST++ dataset contains 5.2 hours of 3D dance motion in 1408 sequences, covering 10 dance genres with multi-view videos with known camera poses—the largest dataset of this kind to our knowledge. We show that naively applying sequence models such as transformers to this dataset for the task of music conditioned 3D motion generation does not produce satisfactory 3D motion that is well correlated with the input music. We overcome these shortcomings by introducing key changes in its architecture design and supervision: FACT model involves a deep cross-modal transformer block with full-attention that is trained to predict N future motions. We empirically show that these changes are key factors in generating long sequences of realistic dance motion that are well-attuned to the input music. We conduct extensive experiments on AIST++ with user studies, where our method outperforms recent state-of-the-art methods both qualitatively and quantitatively. The code and the dataset can be found at: https://google.github.io/aichoreographer.
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“We note that the simple prediction-based late fusion [247], [248] is less adopted in MML Transformers.”
From Multimodal learning with transformers: A survey (2023) · cites this paper as [248]
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404 · cited 8×
“We propose Full Attention Cross-Modal Transformer (FACT) model for the task of 3D dance motion generation.”
BackgroundInfluentialAdam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 1×
“All our experiments are trained with 16 batch size using Adam [45] optimizer.”
MethodJakubův, J., Chvalovský, K., Goertzel, Z., et al. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,648
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173 · cited 2×
“On the other hand, models like BERT [17] employ full-attention for feature learning, but rarely are these models employed in an auto-regressive manner, due to its inefficiency at test time.”
MethodBERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325 · cited 2×
“On the other hand, models like BERT [17] employ full-attention for feature learning, but rarely are these models employed in an auto-regressive manner, due to its inefficiency at test time.”
MethodGANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., & Hochreiter, S. (2017) · arXiv · doi:10.48550/arxiv.1706.08500 · Cited by 8,365
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2929257 · Cited by 5,536 · cited 1×
“Training data for 2D pose and audio is abundant thanks to the high reliability of 2D pose detectors [14].”
BackgroundDeep visual-semantic alignments for generating image descriptions
Karpathy, A., & Fei-Fei, L. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298932 · Cited by 4,902
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
Human3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.248 · Cited by 3,653 · cited 2×
“While there are many large scale 3D motion capture datasets [39, 59, 1, 37], mocap dataset of 3D dance is quite limited as it requires heavy instrumentation and expert dancers for capture.”
BackgroundMethod
Research works printed but not linked: 3
- lamothe, 2019, The dancing species: how moving together in time helps make us human, Aeon
- alemi, 2017, Groovenet: Real-time music-driven dance movement generation using artificial neural networks, Networks, 8, 26
- chen, 2021, Choreomaster: choreography-oriented music-driven dance synthesis, ACM Transactions on Graphics (TOG), 40, 1
Cited by
Sorted by citations
Multimodal learning with transformers: A survey
Xu, P., Zhu, X., & Clifton, D. A. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3275156 · Cited by 1,072
“Depth to Binaural Audio [14], music piece & seed 3D motion to long-range future 3D motions [146], X-raying image & question to answer [244], video & text & audio to text [245]), and”
BackgroundAction-Conditioned 3D Human Motion Synthesis with Transformer VAE
Petrovich, M., Black, M. J., & Varol, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01080 · Cited by 571
“Very recently, [39] presents a Transformer-based method for dance generation conditioned on music and past motion.”
MethodMotionDiffuse: Text-Driven Human Motion Generation With Diffusion Model
Zhang, M., Cai, Z., Pan, L., Hong, F., Guo, X., Yang, L., & Liu, Z. (2024) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2024.3355414 · Cited by 890
“, 2018), 3D keypoints (Ionescu et al., 2013; Joo et al., 2015; Mehta et al., 2017; Trumble et al., 2017; Li et al., 2021) and statistical model parameters (Yu et al.”
BackgroundMotionDiffuse: 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
“, 2018), 3D keypoints (Ionescu et al., 2013; Joo et al., 2015; Mehta et al., 2017; Trumble et al., 2017; Li et al., 2021) and statistical model parameters (Yu et al.”
BackgroundTEMOS: Generating Diverse Human Motions from Textual Descriptions
Petrovich, M., Black, M. J., & Varol, G. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20047-2_28 · Cited by 429
Metadata
- Authors
- Li, R., Yang, S., Ross, D. A., & Kanazawa, A.
- Affiliations
- University of Southern California (US)
- Google (United States) (US)
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 13381-13392
- OpenAlex
- W3204221554
- Cited by
- 553
- References
- 94
- Citations by year
- 2026: 92 · 2025: 146 · 2024: 154 · 2023: 113 · 2022: 46 · 2021: 2
- FWCI
- 117.49
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/CVF International Conference on Computer Vision (ICCV) (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › 3D Shape Modeling and Analysis
- Keywords
- cross-modal transformer, full attention, AIST, dance genres, choreography
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