SyncDiff: Diffusion-based Talking Head Synthesis with Bottlenecked Temporal Visual Prior for Improved Synchronization
This work addresses synchronization issues in talking head synthesis for applications like video generation and virtual avatars, representing an incremental improvement over existing diffusion-based methods.
The paper tackled the problem of lip-speech synchronization in diffusion-based talking head synthesis, which had lower synchronization than GAN-based models, and proposed SyncDiff to improve it by using a bottlenecked temporal visual prior and facial-informative audio features, achieving synchronization scores 27.7% and 62.3% higher than previous diffusion-based methods on LRS2 and LRS3 datasets while maintaining high fidelity.
Talking head synthesis, also known as speech-to-lip synthesis, reconstructs the facial motions that align with the given audio tracks. The synthesized videos are evaluated on mainly two aspects, lip-speech synchronization and image fidelity. Recent studies demonstrate that GAN-based and diffusion-based models achieve state-of-the-art (SOTA) performance on this task, with diffusion-based models achieving superior image fidelity but experiencing lower synchronization compared to their GAN-based counterparts. To this end, we propose SyncDiff, a simple yet effective approach to improve diffusion-based models using a temporal pose frame with information bottleneck and facial-informative audio features extracted from AVHuBERT, as conditioning input into the diffusion process. We evaluate SyncDiff on two canonical talking head datasets, LRS2 and LRS3 for direct comparison with other SOTA models. Experiments on LRS2/LRS3 datasets show that SyncDiff achieves a synchronization score 27.7%/62.3% relatively higher than previous diffusion-based methods, while preserving their high-fidelity characteristics.