# A2V-SLP: Alignment-Aware Variational Modeling for Disentangled Sign Language Production

arXiv: https://arxiv.org/abs/2602.11861 · HTML: https://mlsanity.com/paper/2602.11861/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 25 references that are in the map.
- First posted on arXiv 12 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)
- [Adversarial Training for Multi-Channel Sign Language Production](https://mlsanity.com/paper/2008.12405/) (2020)
- [Gloss Attention for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2307.07361/) (2023)
- [Continuous 3D Multi-Channel Sign Language Production via Progressive Transformers and Mixture Density Networks](https://mlsanity.com/paper/2103.06982/) (2021)
- [G2P-DDM: Generating Sign Pose Sequence from Gloss Sequence with Discrete Diffusion Model](https://mlsanity.com/paper/2208.09141/) (2022)
- [A Data-Driven Representation for Sign Language Production](https://mlsanity.com/paper/2404.11499/) (2024)
- [Improving 3D Pose Estimation for Sign Language](https://mlsanity.com/paper/2308.09525/) (2023)
- [Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production](https://mlsanity.com/paper/2412.13609/) (2024)
- [Autoregressive Sign Language Production: A Gloss-Free Approach with Discrete Representations](https://mlsanity.com/paper/2309.12179/) (2023)
- [Sign Stitching: A Novel Approach to Sign Language Production](https://mlsanity.com/paper/2405.07663/) (2024)
- [Disentangle and Regularize: Sign Language Production with Articulator-Based Disentanglement and Channel-Aware Regularization](https://mlsanity.com/paper/2504.06610/) (2025)
- [Non-Autoregressive Sign Language Production via Knowledge Distillation](https://mlsanity.com/paper/2208.06183/) (2022)
- [SLRTP2025 Sign Language Production Challenge: Methodology, Results, and Future Work](https://mlsanity.com/paper/2508.06951/) (2025)
- [Motion is the Choreographer: Learning Latent Pose Dynamics for Seamless Sign Language Generation](https://mlsanity.com/paper/2508.04049/) (2025)

## Abstract (arXiv)

Building upon recent structural disentanglement frameworks for sign language production, we propose A2V-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.