V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation

V-Express is a progressive training method that employs conditional dropout to balance weak and strong control signals, such as audio and facial pose, in the generation of high-quality portrait videos, showcasing its potential for effective audio-driven video synthesis with room for future enhancements.

Abstract

In the field of portrait video generation, the use of single images to generate portrait videos has become increasingly prevalent. A common approach involves leveraging generative models to enhance adapters for controlled generation. However, control signals (e.g., text, audio, reference image, pose, depth map, etc.) can vary in strength. Among these, weaker conditions often struggle to be effective due to interference from stronger conditions, posing a challenge in balancing these conditions. In our work on portrait video generation, we identified audio signals as particularly weak, often overshadowed by stronger signals such as facial pose and reference image. However, direct training with weak signals often leads to difficulties in convergence. To address this, we propose V-Express, a simple method that balances different control signals through the progressive training and the conditional dropout operation. Our method gradually enables effective control by weak conditions, thereby achieving generation capabilities that simultaneously take into account the facial pose, reference image, and audio. The experimental results demonstrate that our method can effectively generate portrait videos controlled by audio. Furthermore, a potential solution is provided for the simultaneous and effective use of conditions of varying strengths.

Method

The authors of the V-Express paper used a methodology that combines a Latent Diffusion Model (LDM) with a progressive training strategy and conditional dropout operations to balance control signals of varying strengths in portrait video generation. The LDM is used to generate video frames, and the progressive training strategy involves three stages: single-frame generation, multi-frame generation with a focus on audio synchronization, and global fine-tuning. Additionally, they employed training tricks such as mouth loss weight and conditional dropout to improve the effectiveness of weaker control signals like audio and to prevent the model from relying too heavily on stronger signals like reference images and facial keypoints.

Main Finding

The authors discovered that in the field of portrait video generation, control signals such as audio, which are typically weaker, often struggle to be effective due to interference from stronger conditions like facial pose and reference images. To address this, they proposed V-Express, a method that allows for balanced and progressive training by integrating conditional dropout operations. Their experimental results demonstrated that V-Express can effectively generate high-quality portrait videos with synchronized audio, maintaining consistency in facial identity and pose. This method not only enhances the overall quality of the generated videos but also ensures better synchronization and control, providing a potential solution for the simultaneous and effective use of conditions of varying strengths in video generation.

Conclusion

The main conclusion of the V-Express paper is that the proposed method effectively addresses the challenge of balancing weak and strong control signals in portrait video generation, particularly enabling audio signals to have a more pronounced influence on the generation process. By employing a progressive training strategy with conditional dropout operations, V-Express can generate high-quality portrait videos that are synchronized with audio inputs, while maintaining the influence of stronger signals such as facial pose and reference images. The method shows promise for future applications in video synthesis with the potential for further improvements in areas like multilingual support, computational efficiency, and explicit face attribute control.

Keywords

Portrait Video Generation, Conditional Dropout, Progressive Training, Control Signal Balancing, Audio Synchronization, High-Quality Video, Facial Identity Consistency, Facial Pose Control, Mouth Loss Weight, Conditional Dropout Operation, Potential Solution, Multilingual Support, Computational Efficiency, Face Attribute Control