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Motion-Agent: A Conversational Framework for Human Motion Generation with LLMs

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arxiv 2405.17013 v3 pith:ZNXEPW6X submitted 2024-05-27 cs.CV

Qi Wu , Yubo Zhao , Yifan Wang , Xinhang Liu , Yu-Wing Tai , Chi-Keung Tang This is my paper · ORCID
classification cs.CV
keywords motionmotion-agenthumanconversationalgenerationmodelmotionllmframework
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While previous approaches to 3D human motion generation have achieved notable success, they often rely on extensive training and are limited to specific tasks. To address these challenges, we introduce Motion-Agent, an efficient conversational framework designed for general human motion generation, editing, and understanding. Motion-Agent employs an open-source pre-trained language model to develop a generative agent, MotionLLM, that bridges the gap between motion and text. This is accomplished by encoding and quantizing motions into discrete tokens that align with the language model's vocabulary. With only 1--3\% of the model's parameters fine-tuned using adapters, MotionLLM delivers performance on par with diffusion models and other transformer-based methods trained from scratch. By integrating MotionLLM with GPT-4 without additional training, Motion-Agent is able to generate highly complex motion sequences through multi-turn conversations, a capability that previous models have struggled to achieve. Motion-Agent supports a wide range of motion-language tasks, offering versatile capabilities for generating and customizing human motion through interactive conversational exchanges. Project page: https://knoxzhao.github.io/Motion-Agent

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Forward citations

Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. IRG-MotionLLM: Interleaving Motion Generation, Assessment and Refinement for Text-to-Motion Generation

    cs.CV 2025-12 conditional novelty 6.0 of 10

    Interleaving motion generation with text-motion assessment and refinement improves alignment between generated human motion and goal text.

  2. Being-M0.5: A Real-Time Controllable Vision-Language-Motion Model

    cs.CV 2025-08 conditional novelty 6.0 of 10

    Being-M0.5 combines part-aware residual quantization with a 5M-sequence web-video dataset to reach real-time, part-controllable 3D motion generation, though its state-of-the-art claim does not hold on every standard b...

  3. Go to Zero: Towards Zero-shot Motion Generation with Million-scale Data

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A 7B text-to-motion model trained on the new 2M-clip MotionMillion dataset is reported to generalize zero-shot to complex, out-of-domain prompts.

  4. Speaking Beyond Language: A Large-Scale Multimodal Dataset for Learning Nonverbal Cues from Video-Grounded Dialogues

    cs.AI 2025-06 conditional novelty 6.0 of 10

    VENUS is a large podcast-derived dataset aligning text with 3D facial and body cues, and MARS is an LLM fine-tuned on it to generate both words and nonverbal tokens.

  5. MotionLab: Unified Human Motion Generation and Editing via the Motion-Condition-Motion Paradigm

    cs.CV 2025-02 conditional novelty 6.0 of 10

    MotionLab unifies text-based and trajectory-based motion generation with text-based editing, trajectory-based editing, motion in-betweening, and style transfer in one flow-based transformer.

  6. BiomechGPT: Extending Motion-Language Models to Clinical Motion Understanding

    cs.CV 2025-05 conditional novelty 5.0 of 10

    A motion-language model fine-tuned on clinical biomechanics data answers structured questions about movement, scoring highly on activity recognition and gait speed estimation but weaker and underpowered on diagnosis a...

  7. From Motion to Behavior: Hierarchical Modeling of Humanoid Generative Behavior Control

    cs.RO 2025-05 reject novelty 4.0 of 10

    A new 124K-clip dataset with hierarchical text annotations, plus a pipeline that couples an LLM planner, a text-to-pose VAE, diffusion in-betweening, and physics control to generate long-horizon human behaviors.

  8. Human-Centric Foundation Models: Perception, Generation and Agentic Modeling

    cs.CV 2025-02 conditional novelty 4.0 of 10

    A survey proposing a four-part taxonomy for human-centric foundation models and reviewing representative methods in each.

  9. Motion Generation: A Survey of Generative Approaches and Benchmarks

    cs.CV 2025-07 unverdicted novelty 3.0 of 10

    A structured survey that categorizes recent motion generation methods by underlying generative approach and compiles datasets, metrics, and statistical trends.

  10. Multimodal Generative AI with Autoregressive LLMs for Human Motion Understanding and Generation: A Way Forward

    cs.CV 2025-05 conditional novelty 3.0 of 10

    A survey paper reviews multimodal generative AI and autoregressive LLMs for text-driven human motion generation, with comparative tables of models, datasets, and metrics.

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