Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation (2301.03396v2)

Abstract: Talking face generation has historically struggled to produce head movements and natural facial expressions without guidance from additional reference videos. Recent developments in diffusion-based generative models allow for more realistic and stable data synthesis and their performance on image and video generation has surpassed that of other generative models. In this work, we present an autoregressive diffusion model that requires only one identity image and audio sequence to generate a video of a realistic talking human head. Our solution is capable of hallucinating head movements, facial expressions, such as blinks, and preserving a given background. We evaluate our model on two different datasets, achieving state-of-the-art results on both of them.

An Evaluation of Diffused Heads: Advancements in Speech-Driven Facial Animation

The paper "Diffused Heads: Diffusion Models Beat GANs on Talking-Face Generation" presents an innovative approach to face animation driven by speech input, utilizing diffusion models as a more stable and effective alternative to traditional Generative Adversarial Networks (GANs). This work marks a progressive shift in the domain of facial animation, where generating natural and expressive head movements from an audio sequence without additional reference videos has posed significant challenges.

Contextual Overview

Traditionally, facial animation systems aim to create realistic talking-head sequences based on an audio input, offering applications across various fields such as video conferencing, virtual reality, and entertainment. Prior to the emergence of diffusion models, GANs dominated these generative tasks owing to their ability to produce high-quality visuals with customizable control. Despite their success, GANs encounter training difficulties, instability, and occasionally, mode collapse, which hinders their performance in consistently generating nuanced facial expressions and precise lip synchronization.

The paper leverages autoregressive diffusion models, introducing significant methodological enhancements to address the deficiencies observed in GAN-based approaches. By doing so, the authors present a robust framework capable of generating realistic talking-head animations, requiring only a single image and corresponding speech recording.

Methodology and Key Contributions

The paper's primary contribution lies in the deployment of diffusion models, which excel over GANs in synthesizing high-fidelity video. The model, termed "Diffused Heads," synthesizes video frames successively in an autoregressive manner, incorporating novel components such as motion frames and audio embeddings. These elements ensure temporal coherence and identity preservation while enhancing expression realism.

1. Diffusion-Based Generation: Unlike GANs, the diffusion approach employs a probabilistic model to iteratively refine noisy inputs into clear frames, which inherently aids in training stability and reduces the risk of mode collapse.
2. Motion and Audio Conditioning: The introduction of motion frames contributes to smooth transitions, capturing realistic head and lip movements by sampling new frames guided by audio embeddings extracted from temporal models.
3. Lip Sync Enhancement: To handle synchronization between speech and visual output, a novel audio-conditioning mechanism is employed. An additional lip sync loss is incorporated to improve the accuracy of mouth movements, attentive to the nuances of spoken language.

Experimental Evaluation

The model's performance is evaluated on prominent datasets including LRW and CREMA, exhibiting superior expressiveness, fluid motion, and fidelity to both identity and background when compared to baseline methods. The authors report notable advancements across several metrics, such as FVD and FID, which underscore the efficacy of diffusion models in the visual quality of generated sequences.

Implications and Future Prospects

Diffused Heads sets a new benchmark in speech-driven facial animations, suggesting a shift in focus towards diffusion models for realistic video generation tasks. This research exemplifies how diffusion models can overcome the inherent limitations of GANs, thus paving the way for more stable and versatile generative systems.

Looking forward, exploration into more complex multi-modal interactions could further enhance diffusion-based models' ability to deal with diverse expressions, languages, and scenarios, potentially revolutionizing real-time applications. Furthermore, the increased computational efficiency in training and generation phases may lead to broader adoption in commercial and consumer domains.

Conclusion

This paper demonstrates that diffusion models can indeed surpass GANs in the context of speech-driven facial animation, offering stability, quality, and efficiency without compromising the creative capacity of generative tasks. Consequently, it provides a compelling case for adopting diffusion-based methods across a wider range of applications, where realistic and coherent video generation remains a critical objective.