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: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.
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.
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:2609.37400v1 Announce Type: new Abstract: Generating realistic 3D dance from music is a challenging task that requires accurate synchronization with musical rhythms while capturing the spatial...
arXiv:2606. 24307v1 Announce Type: cross Abstract: Interactive music and live performance relies on real-time human expression, but modern generative music AI remains largely absent from this domain due to its prohibitive inference latency and offline rendering paradigm.
Recent advances in generative video modeling have enabled diverse generation, reference-based synthesis, extension, and editing, but existing approaches often rely on fragmented task-specific models. A general model must distinguish heterogeneous target, source, and reference signals to determine what to generate, preserve, or use as guidance, while reducing interference among tasks.
arXiv:2605. 29488v2 Announce Type: replace-cross Abstract: Conditional human motion generation remains a fundamental challenge in computer vision and robotics.
arXiv:2609.23817v1 Announce Type: new Abstract: We present VISTA, a two-stage framework for generating stylized 3D human motion by fusing structural content from text prompts with expressive style fr...
arXiv:2609.08032v1 Announce Type: cross Abstract: We introduce FlexMoGen, a novel framework for flexible human motion synthesis conditioned on both natural language descriptions and motion style refe...
arXiv:2610.00691v1 Announce Type: new Abstract: Recent joint audio-video generative models can synthesize realistic videos with synchronized sound, but typically generate audio as a single mixed trac...
The paper introduces Split-then-Merge (StM), a new framework for generative video composition that improves control and tackles data scarcity. StM divides a large set of unlabeled videos into dynamic foreground and background layers, then self‑composes them to learn how subjects interact with varied scenes. The method employs a transformation‑aware training pipeline with multi‑layer fusion, augmentation, and an identity‑preservation loss, achieving superior performance over state‑of‑the‑art methods in both quantitative and qualitative evaluations.
arXiv:2606. 01703v1 Announce Type: cross Abstract: We address the challenge of generating high-fidelity, long-form soundtracks that remain coherent across scene transitions.