arXiv Computer Vision
BeatDance: Generating Beat-Consistent 3D Dance with Hierarchical Spatial-Temporal Modeling
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
Hugging Face Trending Papers
Jun 25
DanceDuo: Bridging Human Movement and AI Choreography
In recent years, advancements in deep learning and generative models have revolutionized music-driven dance generation. This paper introduces a novel platform, namely DanceDuo, leveraging diffusion models to generate AI-choreographed dance sequences synchronized with a variety of music genres, to encourage dancing practice.
Hugging Face Trending Papers
Jun 29
OmniDance: Multimodal Driven Dance Video Generation with Large-scale Internet Data
Music-driven dance video generation aims to synthesize expressive human motion that is temporally aligned with music while maintaining high visual fidelity. Despite recent progress, existing methods still face two key limitations: the lack of large-scale, high-quality dance video datasets, and the absence of principled frameworks for integrating music as a complementary conditioning signal into Video Generation Foundation Models.
arXiv AI
Jul 7
Text Dictates, Music Decorates: Energy-based Attention for Editable Dance Motion Generation
arXiv:2606. 22726v2 Announce Type: replace Abstract: Choreographic motion generation poses unique challenges for AI, demanding precise semantic control over complex, temporally structured, and expressive full-body dynamics.
arXiv AI
Jul 16
Music-to-Dance Generation via Atomic Movements
arXiv:2607. 13978v1 Announce Type: cross Abstract: Music-driven dance generation aims to produce human motion that is both rhythmically synchronized and semantically consistent with music.
arXiv Computer Vision
Sep 22
Hi-Singers: A Comprehensive High-Quality Dataset for Expressive Audio-Driven Singing Head Synthesis
arXiv:2609.22264v1 Announce Type: cross Abstract: State-of-the-art models for audio-driven digital human generation have achieved photo-realistic results in talking-head synthesis. However, extending...
Hugging Face Trending Papers
Aug 17
Spatial Temporal Synergy: Balancing Change and Invariance in Text Driven 3D Human Motion Editing
Text-driven human motion editing aims to modify existing motion sequences according to natural language instructions while maintaining the structural consistency of the original motion. Existing diffusion-based approaches struggle to balance text-responsive "change" and inertial "invariance".