DisCo: Disentangled Control for Realistic Human Dance Generation

Understand

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.