Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer (2206.12837v2)

Abstract: This paper reports our solution for ACM Multimedia ViCo 2022 Conversational Head Generation Challenge, which aims to generate vivid face-to-face conversation videos based on audio and reference images. Our solution focuses on training a generalized audio-to-head driver using regularization and assembling a high-visual quality renderer. We carefully tweak the audio-to-behavior model and post-process the generated video using our foreground-background fusion module. We get first place in the listening head generation track and second place in the talking head generation track on the official leaderboard. Our code is available at https://github.com/megvii-research/MM2022-ViCoPerceptualHeadGeneration.

• The paper introduces a regularized audio-to-head driver that uses ensemble learning and robust regularization techniques to handle limited training data.
• The enhanced renderer adapts PIRenderer with image boundary inpainting and foreground-background fusion to boost visual fidelity in synthesized videos.
• Empirical results show competitive real-time performance with low FID scores, highlighting its success in the ACM Multimedia ViCo 2022 Head Generation Challenge.

Perceptual Conversational Head Generation with Regularized Driver and Enhanced Renderer

This paper presents a methodological approach for generating perceptual conversational head videos, leveraging advances in audio-visual modeling and rendering technologies. The research contributes a computational solution to the ACM Multimedia ViCo 2022 Conversational Head Generation Challenge, emphasizing the generation of life-like face-to-face conversational videos from audio inputs and reference imagery.

Methodological Innovations

The authors propose a two-pronged solution, focusing primarily on a regularized audio-to-head driver and an enhanced rendering system. The innovations target the challenges in synthesizing high-quality video from limited training data—a critical constraint imposed by the ViCo competition. This led to the application of ensemble learning techniques and model architecture strategies, such as:

• Regularization Techniques: To mitigate overfitting on limited datasets, the authors implemented several neural network regularization strategies. These include residual learning, dropout techniques, and batch normalization with large batch sizes. These methods collectively contribute to a robust audio-to-head driver capable of generalizing well from the scarce data available.
• Enhanced Renderer: The rendering process involved improvements to existing frameworks to produce higher fidelity video outputs. This included the deployment of a portrait image generation model, PIRenderer, which was adapted with customizations such as image boundary inpainting and a foreground-background fusion module. These enhance visual stability and reduce artifacts in synthesized outputs, particularly when dealing with intricate head motions and static backgrounds.

Empirical Evaluation

The proposed solution was rigorously evaluated against a series of metrics, focusing on image quality and semantic accuracy. Notably, performance was quantified using feature-level metrics such as Fréchet Inception Distance (FID), demonstrating superior image synthesis capabilities compared to competing models. The overall system can render at four frames per second, which is pragmatic for real-time applications at a resolution of 256x256 pixels. Results reflected competitive rankings in the ViCo challenge, achieving leading positions in both listening and talking head generation tracks.

Implications and Future Outlook

The research identifies practical implications in digital human systems, particularly regarding responsive digital avatars in virtual interactions and content creation. Furthermore, the work delineates several areas for future exploration:

1. Further Renderer Optimization: Tailoring the renderer more closely to application-specific scenarios might further enhance identity preservation and background consistency.
2. Syllable-to-Lip Mapping: Integrating advancements in the precise correspondence of audio syllables to lip shapes could improve the expressiveness of generated videos, an element that remains conservative in the current framework.
3. Advanced Feature Engineering: Deployment of more sophisticated feature extraction and multi-modal integration techniques could pave the way for even richer synthetic head model capabilities.

In summary, the paper establishes a foundational approach to conversational head video generation, pushing the boundaries of current methodologies in multimedia synthesis through judicious choice and integration of regularization and rendering techniques. This exploration into digital human simulation not only contributes to academic discourse but also propels practical application development in artificial intelligence for communication and entertainment.