Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: AI Choreographer: Music Conditioned 3D Dance Generation with AIST++   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.

28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
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The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat