Multi-Modal Controlled Coherent Motion Generation

• URL: http://arxiv.org/abs/2609.11439v1
• Date: Thu, 10 Sep 2026 12:10:00 GMT
• Title: Multi-Modal Controlled Coherent Motion Generation
• Abstract summary: This paper tackles the challenge of replicating such natural behaviors in 3D avatar motion generation driven by concurrent multimodal inputs.<n>Existing methods, constrained by the scarcity of aligned multimodal data, typically combine motions from individual modalities sequentially or through weighted sums.<n>We propose MOCO, a novel diffusion-based framework capable of processing multiple simultaneous inputs.
• Score: 37.42100177714484
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is natural for humans to walk and talk simultaneously. This paper tackles the challenge of replicating such natural behaviors in 3D avatar motion generation driven by concurrent multimodal inputs, such as a text description of a man walking alongside speech audio. Existing methods, constrained by the scarcity of aligned multimodal data, typically combine motions from individual modalities sequentially or through weighted sums. However, they often result in mismatched or unrealistic movements. To overcome these limitations, we propose MOCO, a novel diffusion-based framework capable of processing multiple simultaneous inputs, including speech audio, text descriptions, and trajectory data, to generate coherent and lifelike motions without requiring aligned multimodal data. Our key innovation lies in decoupling the motion generation process. During each denoising step, the diffusion model independently generates motions for each modality from the input noise and assembles the body parts according to predefined spatial rules. The resulting combined motion is then diffused and serves as the input noise for the subsequent denoising step. This iterative approach enables each modality to refine its contribution within the context of the overall motion, progressively harmonizing movements across modalities. Consequently, the generated motions become increasingly natural and fluid with each iteration, achieving coherent and synchronized behaviors. We evaluate our approach using a purpose-built multimodal benchmark. Experimental results demonstrate that MOCO outperforms existing baselines, advancing the field of multimodal motion generation for 3D avatars.

Related papers

• AnyMo: Scaling Any-Modality Conditional Motion Generation with Masked Modeling [60.13338774249153]
  We introduce OmniHuMo, a dataset of over 5,000 hours of motion and 3.2 million sequences with precisely aligned multimodal annotations.<n>We propose AnyMo, a unified multimodal framework combining a Residual FSQ-based motion tokenizer with a scalable masked modeling transformer.<n>Experiments show that AnyMo achieves high-fidelity synthesis while offering flexible control over both spatial and stylistic attributes.
  arXiv  Detail & Related papers  (2026-05-28T07:15:19Z)
• OmniMotion-X: Versatile Multimodal Whole-Body Motion Generation [52.579531290307926]
  This paper introduces OmniMotion-X, a versatile framework for whole-body human motion generation.<n> OmniMotion-X efficiently supports diverse multimodal tasks, including text-to-motion, music-to-dance, speech-to-gesture.<n>To enable high-quality multimodal training, we construct OmniMoCap-X, the largest unified multimodal motion dataset to date.
  arXiv  Detail & Related papers  (2025-10-22T17:25:33Z)
• OmniMotion: Multimodal Motion Generation with Continuous Masked Autoregression [30.795106931727158]
  Whole-body multi-modal human motion generation poses two primary challenges.<n>We create an effective motion generation mechanism and integrate various modalities, such as text, speech, and music, into a cohesive framework.<n>Our framework outperforms previous methods across all modalities, including text-to-motion, speech-to-gesture, and music-to-dance.
  arXiv  Detail & Related papers  (2025-10-16T17:57:53Z)
• Unified Multi-Modal Interactive & Reactive 3D Motion Generation via Rectified Flow [17.95248351806955]
  We introduce DualFlow, a framework for multi-modal two-person motion generation.<n>It synthesises motion on diverse inputs, including text, music, and prior motion sequences.<n>It produces temporally coherent and rhythmically synchronized motions, setting state-of-the-art in multi-modal human motion generation.
  arXiv  Detail & Related papers  (2025-09-28T22:36:18Z)
• MotionVerse: A Unified Multimodal Framework for Motion Comprehension, Generation and Editing [53.98607267063729]
  MotionVerse is a framework to comprehend, generate, and edit human motion in both single-person and multi-person scenarios.<n>We employ a motion tokenizer with residual quantization, which converts continuous motion sequences into multi-stream discrete tokens.<n>We also introduce a textitDelay Parallel Modeling strategy, which temporally staggers the encoding of residual token streams.
  arXiv  Detail & Related papers  (2025-09-28T04:20:56Z)
• MotionGPT3: Human Motion as a Second Modality [28.616340011811843]
  MotionGPT3 is a bimodal motion-language model for both understanding and generation.<n>A dual-stream Transformer with shared attention preserves modality-specific routes while enabling controlled, bidirectional information flow.<n>Experiments show that MotionGPT3 achieves 2x faster convergence in training loss and up to 4x faster convergence in validation.
  arXiv  Detail & Related papers  (2025-06-30T17:42:22Z)
• GENMO: A GENeralist Model for Human MOtion [64.16188966024542]
  We present GENMO, a unified Generalist Model for Human Motion that bridges motion estimation and generation in a single framework.<n>Our key insight is to reformulate motion estimation as constrained motion generation, where the output motion must precisely satisfy observed conditioning signals.<n>Our novel architecture handles variable-length motions and mixed multimodal conditions (text, audio, video) at different time intervals, offering flexible control.
  arXiv  Detail & Related papers  (2025-05-02T17:59:55Z)
• DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion [70.33381660741861]
  We present DiverseMotion, a new approach for synthesizing high-quality human motions conditioned on textual descriptions. We show that our DiverseMotion achieves the state-of-the-art motion quality and competitive motion diversity.
  arXiv  Detail & Related papers  (2023-09-04T05:43:48Z)
• Executing your Commands via Motion Diffusion in Latent Space [51.64652463205012]
  We propose a Motion Latent-based Diffusion model (MLD) to produce vivid motion sequences conforming to the given conditional inputs. Our MLD achieves significant improvements over the state-of-the-art methods among extensive human motion generation tasks.
  arXiv  Detail & Related papers  (2022-12-08T03:07:00Z)

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.