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arXiv · 2602.11861

A$^{2}$V-SLP: Alignment-Aware Variational Modeling for Disentangled Sign Language Production

Sümeyye Meryem Taşyürek·Enis Mücahid İskender·Hacer Yalim Keles
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Abstract

Building upon recent structural disentanglement frameworks for sign language production, we propose A$^{2}$V-SLP, an alignment-aware variational framework that learns articulator-wise disentangled latent distributions rather than deterministic embeddings. A disentangled Variational Autoencoder (VAE) encodes ground-truth sign pose sequences and extracts articulator-specific mean and variance vectors, which are used as distributional supervision for training a non-autoregressive Transformer. Given text embeddings, the Transformer predicts both latent means and log-variances, while the VAE decoder reconstructs the final sign pose sequences through stochastic sampling at the decoding stage. This formulation maintains articulator-level representations by avoiding deterministic latent collapse through distributional latent modeling. In addition, we integrate a gloss attention mechanism to strengthen alignment between linguistic input and articulated motion. Experimental results show consistent gains over deterministic latent regression, achieving state-of-the-art back-translation performance and improved motion realism in a fully gloss-free setting.

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Sümeyye Meryem Taşyürek, Enis Mücahid İskender, Hacer Yalim Keles. 2026-02-12. A$^{2}$V-SLP: Alignment-Aware Variational Modeling for Disentangled Sign Language Production. https://arxiv.org/abs/2602.11861

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