DiffPortraitVideo: Diffusion-Based Expression-Consistent Zero-Shot Portrait Video Translation

Zero-shot text-to-video diffusion models are crafted to expand pre-trained image diffusion models to the video domain without additional training. In recent times, prevailing techniques commonly rely on existing shapes as constraints and introduce inter-frame attention to ensure texture consistency. However, such shape constraints tend to restrict the stylized geometric deformation of videos and inadvertently neglect the original texture characteristics. Furthermore, existing methods suffer from flickering and inconsistent facial expressions. In this paper, we present DiffPortraitVideo. The framework employs a diffusion model-based feature and attention injection mechanism to generate key frames, with cross-frame constraints to enforce coherence and adaptive feature fusion to ensure expression consistency. Our approach achieves high spatio-temporal and expression consistency while retaining the textual and original image properties. Extensive and comprehensive experiments are conducted to validate the efficacy of our proposed framework in generating personalized, high-quality, and coherent videos. This not only showcases the superiority of our method over existing approaches but also paves the way for further research and development in the field of text-to-video generation with enhanced personalization and quality.