LaMoGen: Laban Movement-Guided Diffusion for Text-to-Motion Generation

• URL: http://arxiv.org/abs/2509.24469v2
• Date: Mon, 13 Oct 2025 04:36:32 GMT
• Title: LaMoGen: Laban Movement-Guided Diffusion for Text-to-Motion Generation
• Authors: Heechang Kim, Gwanghyun Kim, Se Young Chun,
• Abstract summary: 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.
• Score: 28.73306164224967
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diverse human motion generation is an increasingly important task, having various applications in computer vision, human-computer interaction and animation. While text-to-motion synthesis using diffusion models has shown success in generating high-quality motions, achieving fine-grained expressive motion control remains a significant challenge. This is due to the lack of motion style diversity in datasets and the difficulty of expressing quantitative characteristics in natural language. Laban movement analysis has been widely used by dance experts to express the details of motion including motion quality as consistent as possible. Inspired by that, 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. Our proposed zero-shot, inference-time optimization method guides the motion generation model to have desired Laban Effort and Shape components without any additional motion data by updating the text embedding of pretrained diffusion models during the sampling step. We demonstrate that our approach yields diverse expressive motion qualities while preserving motion identity by successfully manipulating motion attributes according to target Laban tags.

Related papers

• 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)
• Towards Synthesized and Editable Motion In-Betweening Through Part-Wise Phase Representation [29.62788252114547]
  styled motion in-betweening is crucial for computer animation and gaming.<n>We propose a novel framework that models motion styles at the body-part level.<n>Our approach enables more nuanced and expressive animations.
  arXiv  Detail & Related papers  (2025-03-11T08:44:27Z)
• 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)
• KinMo: Kinematic-aware Human Motion Understanding and Generation [6.962697597686156]
  Current human motion synthesis frameworks rely on global action descriptions.<n>A single coarse description, such as run, fails to capture details such as variations in speed, limb positioning, and kinematic dynamics.<n>We introduce KinMo, a unified framework built on a hierarchical describable motion representation.
  arXiv  Detail & Related papers  (2024-11-23T06:50:11Z)
• 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)
• Scaling Large Motion Models with Million-Level Human Motions [67.40066387326141]
  We present MotionLib, the first million-level dataset for motion generation.<n>We train a large motion model named projname, demonstrating robust performance across a wide range of human activities.
  arXiv  Detail & Related papers  (2024-10-04T10:48:54Z)
• MotionMix: Weakly-Supervised Diffusion for Controllable Motion Generation [19.999239668765885]
  MotionMix is a weakly-supervised diffusion model that leverages both noisy and unannotated motion sequences. Our framework consistently achieves state-of-the-art performances on text-to-motion, action-to-motion, and music-to-dance tasks.
  arXiv  Detail & Related papers  (2024-01-20T04:58:06Z)
• 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)
• 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)
• 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)
• 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.