AI Choreographer: Music Conditioned 3D Dance Generation with AIST++
Ruilong Li, Shan Yang, David A. Ross, Angjoo Kanazawa

TL;DR
This paper introduces AIST++, a large multi-modal dataset of 3D dance motions and music, and FACT, a transformer-based model that generates realistic, music-conditioned dance sequences, outperforming existing methods.
Contribution
The paper provides the largest dataset of 3D dance motions with multi-view videos and camera poses, and proposes a novel transformer architecture, FACT, for music-conditioned dance generation.
Findings
FACT generates long, realistic dance sequences aligned with music.
The dataset enables better training and evaluation of dance generation models.
FACT outperforms state-of-the-art methods in qualitative and quantitative assessments.
Abstract
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 future motions. We empirically show that these changes are…
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Taxonomy
TopicsHuman Motion and Animation · Human Pose and Action Recognition · Video Analysis and Summarization
MethodsMulti-Head Attention · Attention Is All You Need · Linear Layer · Absolute Position Encodings · Position-Wise Feed-Forward Layer · Dense Connections · Label Smoothing · Residual Connection · Softmax · Adam
