TCDiff++: An End-to-end Trajectory-Controllable Diffusion Model for Harmonious Music-Driven Group Choreography

• URL: http://arxiv.org/abs/2506.18671v3
• Date: Thu, 26 Jun 2025 13:53:17 GMT
• Title: TCDiff++: An End-to-end Trajectory-Controllable Diffusion Model for Harmonious Music-Driven Group Choreography
• Authors: Yuqin Dai, Wanlu Zhu, Ronghui Li, Xiu Li, Zhenyu Zhang, Jun Li, Jian Yang,
• Abstract summary: TCDiff++ is a music-driven end-to-end framework designed to generate harmonious group dance.<n>To mitigate multi-dancer collisions, we utilize a dancer positioning embedding to better maintain the relative positioning among dancers.<n>To address the issue of single-dancer foot sliding, we introduce a swap mode embedding to indicate dancer swapping patterns.
• Score: 33.739289178393925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Music-driven dance generation has garnered significant attention due to its wide range of industrial applications, particularly in the creation of group choreography. During the group dance generation process, however, most existing methods still face three primary issues: multi-dancer collisions, single-dancer foot sliding and abrupt swapping in the generation of long group dance. In this paper, we propose TCDiff++, a music-driven end-to-end framework designed to generate harmonious group dance. Specifically, to mitigate multi-dancer collisions, we utilize a dancer positioning embedding to better maintain the relative positioning among dancers. Additionally, we incorporate a distance-consistency loss to ensure that inter-dancer distances remain within plausible ranges. To address the issue of single-dancer foot sliding, we introduce a swap mode embedding to indicate dancer swapping patterns and design a Footwork Adaptor to refine raw motion, thereby minimizing foot sliding. For long group dance generation, we present a long group diffusion sampling strategy that reduces abrupt position shifts by injecting positional information into the noisy input. Furthermore, we integrate a Sequence Decoder layer to enhance the model's ability to selectively process long sequences. Extensive experiments demonstrate that our TCDiff++ achieves state-of-the-art performance, particularly in long-duration scenarios, ensuring high-quality and coherent group dance generation.

Related papers

• ST-GDance: Long-Term and Collision-Free Group Choreography from Music [40.600668304004344]
  Group dance generation from music has broad applications in film, gaming, and animation production.<n>We propose ST-GDance, a novel framework that decouples spatial and temporal dependencies to optimize long-term and collision-free group choreography.
  arXiv  Detail & Related papers  (2025-07-29T05:54:48Z)
• PAMD: Plausibility-Aware Motion Diffusion Model for Long Dance Generation [51.2555550979386]
  Plausibility-Aware Motion Diffusion (PAMD) is a framework for generating dances that are both musically aligned and physically realistic.<n>To provide more effective guidance during generation, we incorporate Prior Motion Guidance (PMG)<n>Experiments show that PAMD significantly improves musical alignment and enhances the physical plausibility of generated motions.
  arXiv  Detail & Related papers  (2025-05-26T14:44:09Z)
• Scalable Group Choreography via Variational Phase Manifold Learning [8.504657927912076]
  We propose a phase-based variational generative model for group dance generation on learning a generative manifold. Our method achieves high-fidelity group dance motion and enables the generation with an unlimited number of dancers.
  arXiv  Detail & Related papers  (2024-07-26T16:02:37Z)
• Duolando: Follower GPT with Off-Policy Reinforcement Learning for Dance Accompaniment [87.20240797625648]
  We introduce a novel task within the field of 3D dance generation, termed dance accompaniment. It requires the generation of responsive movements from a dance partner, the "follower", synchronized with the lead dancer's movements and the underlying musical rhythm. We propose a GPT-based model, Duolando, which autoregressively predicts the subsequent tokenized motion conditioned on the coordinated information of the music, the leader's and the follower's movements.
  arXiv  Detail & Related papers  (2024-03-27T17:57:02Z)
• Lodge: A Coarse to Fine Diffusion Network for Long Dance Generation Guided by the Characteristic Dance Primitives [50.37531720524434]
  We propose Lodge, a network capable of generating extremely long dance sequences conditioned on given music. Our approach can parallelly generate dance sequences of extremely long length, striking a balance between global choreographic patterns and local motion quality and expressiveness.
  arXiv  Detail & Related papers  (2024-03-15T17:59:33Z)
• Harmonious Group Choreography with Trajectory-Controllable Diffusion [11.704817108195815]
  We propose a Trajectory-Controllable Diffusion (TCDiff) framework to create coherent and harmonious dance movements.<n>To mitigate collisions, we introduce a Dance-Trajectory Navigator that generates collision-free trajectories for multiple dancers.<n>We also present a footwork adaptor that adjusts displacement between frames, supported by a relative forward-kinematic loss.
  arXiv  Detail & Related papers  (2024-03-10T12:11:34Z)
• Controllable Group Choreography using Contrastive Diffusion [9.524877757674176]
  Music-driven group choreography holds significant potential for a wide range of industrial applications. We introduce a Group Contrastive Diffusion (GCD) strategy to enhance the connection between dancers and their group. We demonstrate the effectiveness of our approach in producing visually captivating and consistent group dance motions.
  arXiv  Detail & Related papers  (2023-10-29T11:59:12Z)
• DiffDance: Cascaded Human Motion Diffusion Model for Dance Generation [89.50310360658791]
  We present a novel cascaded motion diffusion model, DiffDance, designed for high-resolution, long-form dance generation. This model comprises a music-to-dance diffusion model and a sequence super-resolution diffusion model. We demonstrate that DiffDance is capable of generating realistic dance sequences that align effectively with the input music.
  arXiv  Detail & Related papers  (2023-08-05T16:18:57Z)
• Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory [92.81383016482813]
  We propose a novel music-to-dance framework, Bailando, for driving 3D characters to dance following a piece of music. We introduce an actor-critic Generative Pre-trained Transformer (GPT) that composes units to a fluent dance coherent to the music. Our proposed framework achieves state-of-the-art performance both qualitatively and quantitatively.
  arXiv  Detail & Related papers  (2022-03-24T13:06:43Z)
• Music-to-Dance Generation with Optimal Transport [48.92483627635586]
  We propose a Music-to-Dance with Optimal Transport Network (MDOT-Net) for learning to generate 3D dance choreographs from music. We introduce an optimal transport distance for evaluating the authenticity of the generated dance distribution and a Gromov-Wasserstein distance to measure the correspondence between the dance distribution and the input music.
  arXiv  Detail & Related papers  (2021-12-03T09:37:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.