  Generative AI has made significant strides in computer vision, particularly
in text-driven image/video synthesis (T2I/T2V). Despite the notable
advancements, it remains challenging in human-centric content synthesis such as
realistic dance generation. Current methodologies, primarily tailored for human
motion transfer, encounter difficulties when confronted with real-world dance
scenarios (e.g., social media dance), which require to generalize across a wide
spectrum of poses and intricate human details. In this paper, we depart from
the traditional paradigm of human motion transfer and emphasize two additional
critical attributes for the synthesis of human dance content in social media
contexts: (i) Generalizability: the model should be able to generalize beyond
generic human viewpoints as well as unseen human subjects, backgrounds, and
poses; (ii) Compositionality: it should allow for the seamless composition of
seen/unseen subjects, backgrounds, and poses from different sources. To address
these challenges, we introduce DISCO, which includes a novel model architecture
with disentangled control to improve the compositionality of dance synthesis,
and an effective human attribute pre-training for better generalizability to
unseen humans. Extensive qualitative and quantitative results demonstrate that
DisCc can generate high-quality human dance images and videos with diverse
appearances and flexible motions. Code is available at
https://disco-dance.github.io/.
