Synthesizing Sign Language Videos with GANs

Summary & Methodology Analysis

To address the difficulty of generating high-fidelity sign language videos using traditional single-discriminator generative adversarial networks, which fail to capture fine-grained details in complex hand and facial movements, the authors propose a multi-expert generator architecture consisting of three parallel encoder-decoder branches. These branches specialize in global, hand, and head visual regions. The system employs three specialized discriminators for global, hand, and head regions to guide their respective generator branches. It incorporates a United Loss consensus mechanism that regularizes each discriminator by blending its individual loss with the ensemble average at a 10% weight. Furthermore, Adaptive Instance Normalization is applied to fuse skeleton structure and style appearance information, and a dual-pathway convolutional-transformer generator block architecture with learnable AdaptiveFeatureFusion is used. A deterministic three-mode alternating training schedule handles discriminator updates, holistic generation, and branch-specialized generation, alongside a Local-Global Merged Attention mechanism for contextual awareness.

The models evaluated include a 0.2B-parameter variant achieving 29.8 peak signal-to-noise ratio and a 1.3B-parameter variant achieving 30.7 peak signal-to-noise ratio on a filtered test set. The related models and datasets mentioned in the context include MD-GAN, Multi parallel U-Net, Swin Transformer, CelebA, GMAN, D2GAN, MCL-GAN, DCGAN, WGAN, Cycle GAN, Style GAN, Style GAN2, and Style GAN3. However, the paper does not specify the exact hardware specifications of the single graphics processing unit used beyond noting the training duration.

Several limitations constrain the current approach. Training the model is computationally expensive, requiring 2-3 months on a single graphics processing unit, which limits the feasibility of conducting full ablation studies. The contribution of United Loss has not been rigorously isolated through controlled experiments on the final v4 architecture. Additionally, the current architecture inference cost scales linearly with the addition of more specialized expert branches. Finally, evaluation is currently limited to peak signal-to-noise ratio metrics, with a comprehensive assessment using perceptual metrics planned but not completed.