Motion is the Choreographer: Learning Latent Pose Dynamics for Seamless Sign Language Generation

• URL: http://arxiv.org/abs/2508.04049v1
• Date: Wed, 06 Aug 2025 03:23:10 GMT
• Title: Motion is the Choreographer: Learning Latent Pose Dynamics for Seamless Sign Language Generation
• Authors: Jiayi He, Xu Wang, Shengeng Tang, Yaxiong Wang, Lechao Cheng, Dan Guo,
• Abstract summary: We propose a new paradigm for sign language video generation that decouples motion semantics from signer identity.<n>First, we construct a signer-independent multimodal motion lexicon, where each gloss is stored as identity-agnostic pose, gesture, and 3D mesh sequences.<n>This compact representation enables our second key innovation: a discrete-to-continuous motion synthesis stage that transforms retrieved gloss sequences into temporally coherent motion trajectories.
• Score: 24.324964949728045
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sign language video generation requires producing natural signing motions with realistic appearances under precise semantic control, yet faces two critical challenges: excessive signer-specific data requirements and poor generalization. We propose a new paradigm for sign language video generation that decouples motion semantics from signer identity through a two-phase synthesis framework. First, we construct a signer-independent multimodal motion lexicon, where each gloss is stored as identity-agnostic pose, gesture, and 3D mesh sequences, requiring only one recording per sign. This compact representation enables our second key innovation: a discrete-to-continuous motion synthesis stage that transforms retrieved gloss sequences into temporally coherent motion trajectories, followed by identity-aware neural rendering to produce photorealistic videos of arbitrary signers. Unlike prior work constrained by signer-specific datasets, our method treats motion as a first-class citizen: the learned latent pose dynamics serve as a portable "choreography layer" that can be visually realized through different human appearances. Extensive experiments demonstrate that disentangling motion from identity is not just viable but advantageous - enabling both high-quality synthesis and unprecedented flexibility in signer personalization.

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