CLUTCH: Contextualized Language model for Unlocking Text-Conditioned Hand motion modelling in the wild

• URL: http://arxiv.org/abs/2602.17770v1
• Date: Thu, 19 Feb 2026 19:02:22 GMT
• Title: CLUTCH: Contextualized Language model for Unlocking Text-Conditioned Hand motion modelling in the wild
• Authors: Balamurugan Thambiraja, Omid Taheri, Radek Danecek, Giorgio Becherini, Gerard Pons-Moll, Justus Thies,
• Abstract summary: We introduce '3D Hands in the Wild' (3D-HIW), a dataset of 32K 3D hand-motion sequences and aligned text.<n>We then propose CLUTCH, an LLM-based hand animation system with two critical innovations: (a) SHIFT, a novel VQ-VAE architecture to tokenize hand motion, and (b) a geometric refinement stage to finetune the LLM.<n> Experiments demonstrate state-of-the-art performance on text-to-motion and motion-to-text tasks, establishing the first benchmark for scalable in-the-wild hand motion modelling
• Score: 41.0322780136795
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Hands play a central role in daily life, yet modeling natural hand motions remains underexplored. Existing methods that tackle text-to-hand-motion generation or hand animation captioning rely on studio-captured datasets with limited actions and contexts, making them costly to scale to "in-the-wild" settings. Further, contemporary models and their training schemes struggle to capture animation fidelity with text-motion alignment. To address this, we (1) introduce '3D Hands in the Wild' (3D-HIW), a dataset of 32K 3D hand-motion sequences and aligned text, and (2) propose CLUTCH, an LLM-based hand animation system with two critical innovations: (a) SHIFT, a novel VQ-VAE architecture to tokenize hand motion, and (b) a geometric refinement stage to finetune the LLM. To build 3D-HIW, we propose a data annotation pipeline that combines vision-language models (VLMs) and state-of-the-art 3D hand trackers, and apply it to a large corpus of egocentric action videos covering a wide range of scenarios. To fully capture motion in-the-wild, CLUTCH employs SHIFT, a part-modality decomposed VQ-VAE, which improves generalization and reconstruction fidelity. Finally, to improve animation quality, we introduce a geometric refinement stage, where CLUTCH is co-supervised with a reconstruction loss applied directly to decoded hand motion parameters. Experiments demonstrate state-of-the-art performance on text-to-motion and motion-to-text tasks, establishing the first benchmark for scalable in-the-wild hand motion modelling. Code, data and models will be released.

Related papers

• SceMoS: Scene-Aware 3D Human Motion Synthesis by Planning with Geometry-Grounded Tokens [89.05195827071582]
  SceMoS is a scene-aware motion synthesis framework.<n>It disentangles global planning from local execution using lightweight 2D cues.<n>SceMoS achieves state-of-the-art motion realism and contact accuracy on the TRUMANS benchmark.
  arXiv  Detail & Related papers  (2026-02-24T02:09:12Z)
• UniMo: Unifying 2D Video and 3D Human Motion with an Autoregressive Framework [54.337290937468175]
  We propose UniMo, an autoregressive model for joint modeling of 2D human videos and 3D human motions within a unified framework.<n>We show that our method simultaneously generates corresponding videos and motions while performing accurate motion capture.
  arXiv  Detail & Related papers  (2025-12-03T16:03:18Z)
• UniHM: Universal Human Motion Generation with Object Interactions in Indoor Scenes [26.71077287710599]
  We propose UniHM, a unified motion language model that leverages diffusion-based generation for scene-aware human motion.<n>UniHM is the first framework to support both Text-to-Motion and Text-to-Human-Object Interaction (HOI) in complex 3D scenes.<n>Our approach introduces three key contributions: (1) a mixed-motion representation that fuses continuous 6DoF motion with discrete local motion tokens to improve motion realism; (2) a novel Look-Up-Free Quantization VAE that surpasses traditional VQ-VAEs in both reconstruction accuracy and generative performance; and (3) an enriched version of
  arXiv  Detail & Related papers  (2025-05-19T07:02:12Z)
• Motion-2-to-3: Leveraging 2D Motion Data to Boost 3D Motion Generation [43.915871360698546]
  2D human videos offer a vast and accessible source of motion data, covering a wider range of styles and activities.<n>We introduce a novel framework that disentangles local joint motion from global movements, enabling efficient learning of local motion priors from 2D data.<n>Our method efficiently utilizes 2D data, supporting realistic 3D human motion generation and broadening the range of motion types it supports.
  arXiv  Detail & Related papers  (2024-12-17T17:34:52Z)
• MotionFix: Text-Driven 3D Human Motion Editing [52.11745508960547]
  Key challenges include the scarcity of training data and the need to design a model that accurately edits the source motion. We propose a methodology to semi-automatically collect a dataset of triplets comprising (i) a source motion, (ii) a target motion, and (iii) an edit text. Access to this data allows us to train a conditional diffusion model, TMED, that takes both the source motion and the edit text as input.
  arXiv  Detail & Related papers  (2024-08-01T16:58:50Z)
• Generating Human Interaction Motions in Scenes with Text Control [66.74298145999909]
  We present TeSMo, a method for text-controlled scene-aware motion generation based on denoising diffusion models. Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model. To facilitate training, we embed annotated navigation and interaction motions within scenes.
  arXiv  Detail & Related papers  (2024-04-16T16:04:38Z)
• Learning Human Motion from Monocular Videos via Cross-Modal Manifold Alignment [45.74813582690906]
  Learning 3D human motion from 2D inputs is a fundamental task in the realms of computer vision and computer graphics. We present the Video-to-Motion Generator (VTM), which leverages motion priors through cross-modal latent feature space alignment. The VTM showcases state-of-the-art performance in reconstructing 3D human motion from monocular videos.
  arXiv  Detail & Related papers  (2024-04-15T06:38:09Z)
• Generating Holistic 3D Human Motion from Speech [97.11392166257791]
  We build a high-quality dataset of 3D holistic body meshes with synchronous speech. We then define a novel speech-to-motion generation framework in which the face, body, and hands are modeled separately.
  arXiv  Detail & Related papers  (2022-12-08T17:25:19Z)
• Being Comes from Not-being: Open-vocabulary Text-to-Motion Generation with Wordless Training [178.09150600453205]
  In this paper, we investigate offline open-vocabulary text-to-motion generation in a zero-shot learning manner. Inspired by the prompt learning in NLP, we pretrain a motion generator that learns to reconstruct the full motion from the masked motion. Our method reformulates the input text into a masked motion as the prompt for the motion generator to reconstruct'' the motion.
  arXiv  Detail & Related papers  (2022-10-28T06:20:55Z)

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.