SMooGPT: Stylized Motion Generation using Large Language Models

• URL: http://arxiv.org/abs/2509.04058v1
• Date: Thu, 04 Sep 2025 09:41:18 GMT
• Title: SMooGPT: Stylized Motion Generation using Large Language Models
• Authors: Lei Zhong, Yi Yang, Changjian Li,
• Abstract summary: Stylized motion generation is actively studied in computer graphics, especially benefiting from the rapid advances in diffusion models.<n>Existing research attempts to address this problem via motion style transfer or conditional motion generation.<n>We propose utilizing body-part text space as an intermediate representation, and present SMooGPT.
• Score: 23.476473154719514
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Stylized motion generation is actively studied in computer graphics, especially benefiting from the rapid advances in diffusion models. The goal of this task is to produce a novel motion respecting both the motion content and the desired motion style, e.g., ``walking in a loop like a Monkey''. Existing research attempts to address this problem via motion style transfer or conditional motion generation. They typically embed the motion style into a latent space and guide the motion implicitly in a latent space as well. Despite the progress, their methods suffer from low interpretability and control, limited generalization to new styles, and fail to produce motions other than ``walking'' due to the strong bias in the public stylization dataset. In this paper, we propose to solve the stylized motion generation problem from a new perspective of reasoning-composition-generation, based on our observations: i) human motion can often be effectively described using natural language in a body-part centric manner, ii) LLMs exhibit a strong ability to understand and reason about human motion, and iii) human motion has an inherently compositional nature, facilitating the new motion content or style generation via effective recomposing. We thus propose utilizing body-part text space as an intermediate representation, and present SMooGPT, a fine-tuned LLM, acting as a reasoner, composer, and generator when generating the desired stylized motion. Our method executes in the body-part text space with much higher interpretability, enabling fine-grained motion control, effectively resolving potential conflicts between motion content and style, and generalizes well to new styles thanks to the open-vocabulary ability of LLMs. Comprehensive experiments and evaluations, and a user perceptual study, demonstrate the effectiveness of our approach, especially under the pure text-driven stylized motion generation.

Related papers

• LaMoGen: Laban Movement-Guided Diffusion for Text-to-Motion Generation [28.73306164224967]
  This work aims for interpretable and expressive control of human motion generation by seamlessly integrating the quantification methods of Laban Effort and Shape components into the text-guided motion generation models.<n>Our approach yields diverse expressive motion qualities while preserving motion identity by manipulating motion attributes according to target Laban tags.
  arXiv  Detail & Related papers  (2025-09-29T08:48:49Z)
• Motion Prompting: Controlling Video Generation with Motion Trajectories [57.049252242807874]
  We train a video generation model conditioned on sparse or dense video trajectories.<n>We translate high-level user requests into detailed, semi-dense motion prompts.<n>We demonstrate our approach through various applications, including camera and object motion control, "interacting" with an image, motion transfer, and image editing.
  arXiv  Detail & Related papers  (2024-12-03T18:59:56Z)
• MotionGPT-2: A General-Purpose Motion-Language Model for Motion Generation and Understanding [76.30210465222218]
  MotionGPT-2 is a unified Large Motion-Language Model (LMLMLM) It supports multimodal control conditions through pre-trained Large Language Models (LLMs) It is highly adaptable to the challenging 3D holistic motion generation task.
  arXiv  Detail & Related papers  (2024-10-29T05:25:34Z)
• DartControl: A Diffusion-Based Autoregressive Motion Model for Real-Time Text-Driven Motion Control [12.465927271402442]
  Text-conditioned human motion generation allows for user interaction through natural language.<n>DartControl is a Diffusion-based Autoregressive motion primitive model for Real-time Text-driven motion control.<n>Our model effectively learns a compact motion primitive space jointly conditioned on motion history and text inputs.
  arXiv  Detail & Related papers  (2024-10-07T17:58:22Z)
• Monkey See, Monkey Do: Harnessing Self-attention in Motion Diffusion for Zero-shot Motion Transfer [55.109778609058154]
  Existing diffusion-based motion editing methods overlook the profound potential of the prior embedded within the weights of pre-trained models. We uncover the roles and interactions of attention elements in capturing and representing motion patterns. We integrate these elements to transfer a leader motion to a follower one while maintaining the nuanced characteristics of the follower, resulting in zero-shot motion transfer.
  arXiv  Detail & Related papers  (2024-06-10T17:47:14Z)
• MotionLLM: Understanding Human Behaviors from Human Motions and Videos [40.132643319573205]
  This study delves into the realm of multi-modality (i.e., video and motion modalities) human behavior understanding. We present MotionLLM, a framework for human motion understanding, captioning, and reasoning.
  arXiv  Detail & Related papers  (2024-05-30T17:59:50Z)
• MotionCrafter: One-Shot Motion Customization of Diffusion Models [66.44642854791807]
  We introduce MotionCrafter, a one-shot instance-guided motion customization method. MotionCrafter employs a parallel spatial-temporal architecture that injects the reference motion into the temporal component of the base model. During training, a frozen base model provides appearance normalization, effectively separating appearance from motion.
  arXiv  Detail & Related papers  (2023-12-08T16:31:04Z)
• Universal Humanoid Motion Representations for Physics-Based Control [71.46142106079292]
  We present a universal motion representation that encompasses a comprehensive range of motor skills for physics-based humanoid control. We first learn a motion imitator that can imitate all of human motion from a large, unstructured motion dataset. We then create our motion representation by distilling skills directly from the imitator.
  arXiv  Detail & Related papers  (2023-10-06T20:48:43Z)
• Priority-Centric Human Motion Generation in Discrete Latent Space [59.401128190423535]
  We introduce a Priority-Centric Motion Discrete Diffusion Model (M2DM) for text-to-motion generation. M2DM incorporates a global self-attention mechanism and a regularization term to counteract code collapse. We also present a motion discrete diffusion model that employs an innovative noise schedule, determined by the significance of each motion token.
  arXiv  Detail & Related papers  (2023-08-28T10:40:16Z)
• MotionGPT: Human Motion as a Foreign Language [47.21648303282788]
  Human motion displays a semantic coupling akin to human language, often perceived as a form of body language. By fusing language data with large-scale motion models, motion-language pre-training can enhance the performance of motion-related tasks. We propose MotionGPT, a unified, versatile, and user-friendly motion-language model to handle multiple motion-relevant tasks.
  arXiv  Detail & Related papers  (2023-06-26T15:53:02Z)
• MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model [35.32967411186489]
  MotionDiffuse is a diffusion model-based text-driven motion generation framework. It excels at modeling complicated data distribution and generating vivid motion sequences. It responds to fine-grained instructions on body parts, and arbitrary-length motion synthesis with time-varied text prompts.
  arXiv  Detail & Related papers  (2022-08-31T17:58:54Z)

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.