The Impact of VAE Design on Latent Pose Representations for Diffusion-based Sign Language Production

Researchers investigate how variational autoencoder (VAE) design choices affect latent space properties in sign language production systems using diffusion models. Testing on the Phoenix14T dataset reveals that downstream generative performance correlates more strongly with latent space structure than with traditional reconstruction metrics, suggesting current evaluation methods may miss critical factors influencing model quality.

This research addresses a fundamental gap in how generative models for sign language production are evaluated and optimized. Sign language production through latent diffusion requires an initial encoding stage where pose sequences are compressed into a latent space, but the field has relied primarily on geometric reconstruction metrics that don't capture whether the encoded space facilitates effective generative modeling downstream. The study systematically examines how different VAE architectural choices and training objectives shape latent space properties, then measures their actual impact on text-to-sign generation quality using back-translation BLEU scores.

The findings challenge conventional wisdom in the machine learning pipeline approach to sign language technology. Rather than optimizing the autoencoder in isolation using standard reconstruction metrics, the research demonstrates that latent space structure—properties like distribution smoothness, clustering behavior, and semantic organization—better predicts downstream diffusion model performance. This represents a shift toward end-to-end optimization thinking where intermediate component evaluation must align with final task objectives.

For developers and researchers working on accessibility technologies, this work has immediate practical implications. Current VAE designs may produce visually accurate pose reconstructions while creating latent spaces poorly suited for diffusion-based generation, leading to suboptimal sign language video synthesis. Organizations developing sign language production tools should reconsider their evaluation pipelines and potentially redesign encoding architectures based on latent space properties rather than isolated reconstruction accuracy. This research establishes that thoughtful design of representation learning components pays dividends in generative model quality.

• →VAE latent space structure properties better predict diffusion model performance than traditional reconstruction metrics
• →Architectural and training objective choices in autoencoders significantly impact downstream generative modeling capabilities
• →Current evaluation methods for sign language encoding systems may fail to capture critical factors affecting generation quality
• →End-to-end optimization considering both encoding and generation stages outperforms isolated component optimization
• →Sign language production systems require tailored VAE designs that prioritize latent space organization over reconstruction accuracy