Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations

• URL: http://arxiv.org/abs/2309.12179v2
• Date: Sat, 8 Jun 2024 12:33:11 GMT
• Title: Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations
• Authors: Eui Jun Hwang, Huije Lee, Jong C. Park,
• Abstract summary: Gloss-free Sign Language Production (SLP) offers a direct translation of spoken language sentences into sign language. This paper presents a novel approach to SLP that leverages Vector Quantization to derive discrete representations from sign pose sequences.
• Score: 8.254354613959224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gloss-free Sign Language Production (SLP) offers a direct translation of spoken language sentences into sign language, bypassing the need for gloss intermediaries. This paper presents the Sign language Vector Quantization Network, a novel approach to SLP that leverages Vector Quantization to derive discrete representations from sign pose sequences. Our method, rooted in both manual and non-manual elements of signing, supports advanced decoding methods and integrates latent-level alignment for enhanced linguistic coherence. Through comprehensive evaluations, we demonstrate superior performance of our method over prior SLP methods and highlight the reliability of Back-Translation and Fr\'echet Gesture Distance as evaluation metrics.

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