Semantic-driven diffusion for sign language production with gloss-pose latent spaces alignment

Abstract

Sign Language Production (SLP) aims to translate spoken language into visual sign language sequences. The most challenging process in SLP is the transformation of a sequence of sign glosses into corresponding sign poses (G2P). Existing approaches on G2P mainly focus on constructing mappings of sign language glosses to frame-level sign pose representations, while neglecting gloss is just a weak annotation of the sequence of sign poses. To address this problem, this paper proposes the semantic-driven diffusion model with gloss-pose latent spaces alignment (SDD-GPLA) for G2P. G2P is divided into two phases. In the first phase, we design the gloss-pose latent spaces alignment (GPLA) to model the sign pose latent representations with glosses dependency. In the second phase, we propose semantic-driven diffusion (SDD) with supervised pose reconstruction guidance as a mapping between the gloss and sign poses latent features. In addition, we propose the sign pose decoder (${\text{Decoder}}^p$) to progressively generate high-resolution sign poses from latent sign pose features and to guide the SDD training process. We evaluated SDD-GPLA on a self-collected dataset of Daily Chinese Sign Language (DCSL) and a public dataset called RWTH-Phoenix-Weather-2014T. Compared with the state-of-the-art G2P methods, we obtain at least 22.9% and 2.3% improvement in WER scores on the above two datasets, respectively.