ReactDance: Hierarchical Representation for High-Fidelity and Coherent Long-Form Reactive Dance Generation

• URL: http://arxiv.org/abs/2505.05589v2
• Date: Mon, 29 Sep 2025 10:59:21 GMT
• Title: ReactDance: Hierarchical Representation for High-Fidelity and Coherent Long-Form Reactive Dance Generation
• Authors: Jingzhong Lin, Xinru Li, Yuanyuan Qi, Bohao Zhang, Wenxiang Liu, Kecheng Tang, Wenxuan Huang, Xiangfeng Xu, Bangyan Li, Changbo Wang, Gaoqi He,
• Abstract summary: We introduce textbfReactDance, a diffusion framework that operates on a novel hierarchical latent space.<n>We show that ReactDance substantially outperforms state-of-the-art methods in motion quality, long-term coherence, and sampling efficiency.
• Score: 21.732021702335345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reactive dance generation (RDG), the task of generating a dance conditioned on a lead dancer's motion, holds significant promise for enhancing human-robot interaction and immersive digital entertainment. Despite progress in duet synchronization and motion-music alignment, two key challenges remain: generating fine-grained spatial interactions and ensuring long-term temporal coherence. In this work, we introduce \textbf{ReactDance}, a diffusion framework that operates on a novel hierarchical latent space to address these spatiotemporal challenges in RDG. First, for high-fidelity spatial expression and fine-grained control, we propose Hierarchical Finite Scalar Quantization (\textbf{HFSQ}). This multi-scale motion representation effectively disentangles coarse body posture from subtle limb dynamics, enabling independent and detailed control over both aspects through a layered guidance mechanism. Second, to efficiently generate long sequences with high temporal coherence, we propose Blockwise Local Context (\textbf{BLC}), a non-autoregressive sampling strategy. Departing from slow, frame-by-frame generation, BLC partitions the sequence into blocks and synthesizes them in parallel via periodic causal masking and positional encodings. Coherence across these blocks is ensured by a dense sliding-window training approach that enriches the representation with local temporal context. Extensive experiments show that ReactDance substantially outperforms state-of-the-art methods in motion quality, long-term coherence, and sampling efficiency.

Related papers

• HINT: Hierarchical Interaction Modeling for Autoregressive Multi-Human Motion Generation [55.73037290387896]
  We introduce HINT, the first autoregressive framework for multi-human motion generation with Hierarchical INTeraction modeling in diffusion.<n>First, HINT leverages a disentangled motion representation within a canonicalized latent space, decoupling local motion semantics from inter-person interactions.<n>Second, HINT adopts a sliding-window strategy for efficient online generation, and aggregates local within-window and global cross-window conditions to capture past human history, inter-person dependencies, and align with text guidance.
  arXiv  Detail & Related papers  (2026-01-28T08:47:23Z)
• Towards Arbitrary Motion Completing via Hierarchical Continuous Representation [64.6525112550758]
  We propose a novel parametric activation-induced hierarchical implicit representation framework, called NAME, based on Implicit Representations (INRs)<n>Our method introduces a hierarchical temporal encoding mechanism that extracts features from motion sequences at multiple temporal scales, enabling effective capture of intricate temporal patterns.
  arXiv  Detail & Related papers  (2025-12-24T14:07:04Z)
• Towards Robust and Generalizable Continuous Space-Time Video Super-Resolution with Events [71.2439653098351]
  Continuous space-time video super-STVSR has garnered increasing interest for its capability to reconstruct high-resolution and high-frame-rate videos at arbitrary temporal scales.<n>We present EvEnhancer, a novel approach that marries unique properties of high temporal and high dynamic range encapsulated in event streams.<n>Our method achieves state-of-the-art performance on both synthetic and real-world datasets, while maintaining generalizability at OOD scales.
  arXiv  Detail & Related papers  (2025-10-04T15:23:07Z)
• Towards Consistent Long-Term Pose Generation [0.0]
  We propose a novel one-stage architecture that directly generates poses in continuous coordinate space from minimal context.<n>Our key innovation is eliminating the need for intermediate representations or token-based generation.<n>Our approach significantly outperforms existing quantization-based and autoregressive methods, especially in long-term generation scenarios.
  arXiv  Detail & Related papers  (2025-07-24T12:57:22Z)
• Identity-Preserving Text-to-Video Generation Guided by Simple yet Effective Spatial-Temporal Decoupled Representations [131.33758144860988]
  Identity-preserving text-to-video (IPT2V) generation aims to create high-fidelity videos with consistent human identity.<n>Current end-to-end frameworks suffer a critical spatial-temporal trade-off.<n>We propose a simple yet effective spatial-temporal decoupled framework that decomposes representations into spatial features for layouts and temporal features for motion dynamics.
  arXiv  Detail & Related papers  (2025-07-07T06:54:44Z)
• MotionRAG-Diff: A Retrieval-Augmented Diffusion Framework for Long-Term Music-to-Dance Generation [10.203209816178552]
  MotionRAG-Diff is a hybrid framework that integrates Retrieval-Augmented Generation and diffusion-based refinement.<n>Our method introduces three core innovations.<n>It achieves state-of-the-art performance in motion quality, diversity, and music-motion synchronization accuracy.
  arXiv  Detail & Related papers  (2025-06-03T09:12:48Z)
• EHGCN: Hierarchical Euclidean-Hyperbolic Fusion via Motion-Aware GCN for Hybrid Event Stream Perception [44.8109315513885]
  Event cameras emit high-speed event stream for perception tasks.<n>We propose a novel approach named EHGCN to perceive event stream in both Euclidean and hyperbolic spaces.
  arXiv  Detail & Related papers  (2025-04-23T11:01:03Z)
• FreSca: Scaling in Frequency Space Enhances Diffusion Models [55.75504192166779]
  This paper explores frequency-based control within latent diffusion models.<n>We introduce FreSca, a novel framework that decomposes noise difference into low- and high-frequency components.<n>FreSca operates without any model retraining or architectural change, offering model- and task-agnostic control.
  arXiv  Detail & Related papers  (2025-04-02T22:03:11Z)
• ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer [58.49950218437718]
  We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech.<n>The core innovation lies in the Recurrent Embedded Transformer (RET), which integrates Dynamic Embedding Regularization (DER) into a Vision Transformer (ViT) core architecture.<n>To enhance model robustness, we incorporate the proposed DER strategy, which equips the model with dual capabilities of noise resistance and cross-domain generalization.
  arXiv  Detail & Related papers  (2025-03-27T16:39:40Z)
• Streaming Generation of Co-Speech Gestures via Accelerated Rolling Diffusion [0.881371061335494]
  We introduce Accelerated Rolling Diffusion, a novel framework for streaming gesture generation.<n>RDLA restructures the noise schedule into a stepwise ladder, allowing multiple frames to be denoised simultaneously.<n>This significantly improves sampling efficiency while maintaining motion consistency, achieving up to a 2x speedup.
  arXiv  Detail & Related papers  (2025-03-13T15:54:45Z)
• GestureLSM: Latent Shortcut based Co-Speech Gesture Generation with Spatial-Temporal Modeling [32.47567372398872]
  GestureLSM is a flow-matching-based approach for Co-Speech Gesture Generation with spatial-temporal modeling.<n>It achieves state-of-the-art performance on BEAT2 while significantly reducing inference time compared to existing methods.
  arXiv  Detail & Related papers  (2025-01-31T05:34:59Z)
• Building a Multi-modal Spatiotemporal Expert for Zero-shot Action Recognition with CLIP [34.88916568947695]
  We propose a novel CLI framework to comprehend multi-temporal dynamics.<n>For the vision side, we propose an efficient Dynamic Crossshot Attention.<n>For the semantic side, we conduct text augmentation by constructing an Action Knowledge Graph.
  arXiv  Detail & Related papers  (2024-12-13T06:30:52Z)
• Lagrangian Motion Fields for Long-term Motion Generation [51.02126882968116]
  We introduce the concept of Lagrangian Motion Fields, specifically designed for long-term motion generation.<n>By treating each joint as a Lagrangian particle with uniform velocity over short intervals, our approach condenses motion representations into a series of "supermotions"<n>Our solution is versatile and lightweight, eliminating the need for neural network preprocessing.
  arXiv  Detail & Related papers  (2024-09-03T01:38:06Z)
• Stragglers-Aware Low-Latency Synchronous Federated Learning via Layer-Wise Model Updates [71.81037644563217]
  Synchronous federated learning (FL) is a popular paradigm for collaborative edge learning. As some of the devices may have limited computational resources and varying availability, FL latency is highly sensitive to stragglers. We propose straggler-aware layer-wise federated learning (SALF) that leverages the optimization procedure of NNs via backpropagation to update the global model in a layer-wise fashion.
  arXiv  Detail & Related papers  (2024-03-27T09:14:36Z)
• DiffusionPhase: Motion Diffusion in Frequency Domain [69.811762407278]
  We introduce a learning-based method for generating high-quality human motion sequences from text descriptions. Existing techniques struggle with motion diversity and smooth transitions in generating arbitrary-length motion sequences. We develop a network encoder that converts the motion space into a compact yet expressive parameterized phase space.
  arXiv  Detail & Related papers  (2023-12-07T04:39:22Z)
• Dance Your Latents: Consistent Dance Generation through Spatial-temporal Subspace Attention Guided by Motion Flow [22.1733448870831]
  We present Dance--Latents, a framework that makes latents dance coherently following motion flow to generate consistent dance videos. Experimental results in TikTok dataset demonstrate that our approach significantly enhancestemporal consistency of irregular generated videos.
  arXiv  Detail & Related papers  (2023-10-20T12:53:08Z)
• Temporal Context Aggregation Network for Temporal Action Proposal Refinement [93.03730692520999]
  Temporal action proposal generation is a challenging yet important task in the video understanding field. Current methods still suffer from inaccurate temporal boundaries and inferior confidence used for retrieval. We propose TCANet to generate high-quality action proposals through "local and global" temporal context aggregation.
  arXiv  Detail & Related papers  (2021-03-24T12:34:49Z)
• Multi-Temporal Convolutions for Human Action Recognition in Videos [83.43682368129072]
  We present a novel temporal-temporal convolution block that is capable of extracting at multiple resolutions. The proposed blocks are lightweight and can be integrated into any 3D-CNN architecture.
  arXiv  Detail & Related papers  (2020-11-08T10:40:26Z)
• Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition [79.33539539956186]
  We propose a simple method to disentangle multi-scale graph convolutions and a unified spatial-temporal graph convolutional operator named G3D. By coupling these proposals, we develop a powerful feature extractor named MS-G3D based on which our model outperforms previous state-of-the-art methods on three large-scale datasets.
  arXiv  Detail & Related papers  (2020-03-31T11:28:25Z)

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.