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

• URL: http://arxiv.org/abs/2602.11861v1
• Date: Thu, 12 Feb 2026 12:07:32 GMT
• Title: A$^{2}$V-SLP: Alignment-Aware Variational Modeling for Disentangled Sign Language Production
• Authors: Sümeyye Meryem Taşyürek, Enis Mücahid İskender, Hacer Yalim Keles,
• Abstract summary: A$2$V-SLP learns articulator-wise disentangled latent distributions rather than deterministic embeddings.<n>A disentangled Variational Autoencoder encodes ground-truth sign pose sequences and extracts articulator-specific mean and variance vectors.
• Score: 0.9384603486206738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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