Naturalness-guided Manifold Flow Matching for Sign Language Production
Sign Language Production (SLP) aims to generate sign motions from text. Conditional Flow Matching methods have achieved strong performance in SLP by constructing conditional paths that transform a source distribution into a target distribution. However, existing methods construct these paths via linear interpolation, whereas the rotational geometry of human joints confines valid joint rotations to a manifold embedded in Euclidean space. Consequently, linear interpolation between two sign motions leaves this manifold and ignores the motion distribution on it. In this paper, we revisit SLP from the perspective of manifold transport and propose a Naturalness-guided Manifold Flow Matching fra...
| Authors | Jiayi He, Shengeng Tang, Sisi You et al. |
|---|---|
| Primary category | cs.AI |
| Published | 27 September 2026 |
| Last updated at source | 27 September 2026 |
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