Title: CLUTCH: Contextualized Language model for Unlocking Text-Conditioned Hand motion modelling in the wild
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: Human Motion Synthesis, Hand motion synthesis, LLM, Motion in-the-wild

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.

TL;DR: CLUTCH is an LLM-based model designed to synthesize and caption natural, in-the-wild 3D hand motions.