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Sign Language Production (SLP) is the tough task of turning sign languageinto sign videos. The main goal of SLP is to create these videos using a signgloss. In this research, we've developed a new method to make high-quality signvideos without using human poses as a middle step. Our model works in two mainparts: first, it learns from a generator and the video's hidden features, andnext, it uses another model to understand the order of these hidden features.To make this method even better for sign videos, we make several significantimprovements. (i) In the first stage, we take an improved 3D VQ-GAN to learndownsampled latent representations. (ii) In the second stage, we introducesequence-to-sequence attention to better leverage conditional information.(iii) The separated two-stage training discards the realistic visual semanticof the latent codes in the second stage. To endow the latent sequences semanticinformation, we extend the token-level autoregressive latent codes learningwith perceptual loss and reconstruction loss for the prior model with visualperception. Compared with previous state-of-the-art approaches, our modelperforms consistently better on two word-level sign language datasets, i.e.,WLASL and NMFs-CSL.

Sign Language Production with Latent Motion Transformer