One-sentence Summary

Addressing the inaccurate style aggregation of prior methods, this work proposes an audio-aware style reference scheme that integrates a Transformer-based lip motion predictor enhanced by cross-attention layers for style aggregation and a conditional latent diffusion renderer fused via modulated convolutions and spatial cross-attention, with extensive experiments validating its ability to achieve precise lip synchronization, preserve individual speaking styles, and generate high-fidelity talking face videos.

Key Contributions

• This work proposes an audio-aware style reference scheme that models the relationship between input audio and reference audio to preserve individual speaking styles. A Transformer-based architecture predicts target lip motions by aggregating personalized style cues through cross-attention layers.
• A conditional latent diffusion model renders the predicted lip motions into realistic talking face videos. This renderer integrates motion signals through modulated convolutional layers and fuses reference facial images via spatial cross-attention mechanisms.
• Extensive experiments validate that the proposed framework achieves precise lip synchronization, effectively preserves individual speaking styles, and generates high-fidelity talking face videos. The results confirm the effectiveness of the integrated style aggregation and rendering pipeline.

Introduction

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