DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models (2312.09767v3)

Abstract: Emotional talking head generation has attracted growing attention. Previous methods, which are mainly GAN-based, still struggle to consistently produce satisfactory results across diverse emotions and cannot conveniently specify personalized emotions. In this work, we leverage powerful diffusion models to address the issue and propose DreamTalk, a framework that employs meticulous design to unlock the potential of diffusion models in generating emotional talking heads. Specifically, DreamTalk consists of three crucial components: a denoising network, a style-aware lip expert, and a style predictor. The diffusion-based denoising network can consistently synthesize high-quality audio-driven face motions across diverse emotions. To enhance lip-motion accuracy and emotional fullness, we introduce a style-aware lip expert that can guide lip-sync while preserving emotion intensity. To more conveniently specify personalized emotions, a diffusion-based style predictor is utilized to predict the personalized emotion directly from the audio, eliminating the need for extra emotion reference. By this means, DreamTalk can consistently generate vivid talking faces across diverse emotions and conveniently specify personalized emotions. Extensive experiments validate DreamTalk's effectiveness and superiority. The code is available at https://github.com/ali-vilab/dreamtalk.

• The paper introduces a diffusion-based framework that overcomes GAN limitations to generate lifelike, expressive talking heads.
• It employs a denoising network and a style-aware lip expert for precise audio-driven facial animations and improved lip synchronization.
• Experimental results showcase superior performance over state-of-the-art methods, validated by metrics like SSIM, CPBD, F-LMD, and SyncNet scores.

An Overview of DreamTalk: Integration of Diffusion Models in Talking Head Generation

The paper "DreamTalk: When Expressive Talking Head Generation Meets Diffusion Probabilistic Models" introduces a novel framework that harnesses the power of diffusion probabilistic models for generating expressive talking heads driven by audio inputs. The framework is composed of three pivotal components: a denoising network, a style-aware lip expert, and a style predictor, aimed at alleviating the challenges prevailing in expressive talking head generation.

The underlying motivation for DreamTalk is rooted in the limitations of generative adversarial networks (GANs), which currently lead the research in talking head generation but suffer from mode collapse and unstable training. By pivoting to diffusion models, the authors of DreamTalk aim to mitigate these challenges, leveraging their superior distribution learning capabilities, known for producing high-quality results across various generative tasks.

Key Components of DreamTalk

The DreamTalk framework is systematically delineated as follows:

1. Denoising Network: The cornerstone of DreamTalk is a diffusion-based denoising network that crafts audio-driven facial animations. This network capitalizes on diffusion models' inherent property of robust distribution learning, enabling the generation of high-quality facial motions mirroring diverse expressions as dictated by the input audio.
2. Style-Aware Lip Expert: Notably, DreamTalk integrates a style-aware lip expert designed to improve lip synchronization while preserving expression authenticity. Unlike traditional lip experts focusing merely on synchronization, this style-aware extension enhances both lip accuracy and expressiveness by considering the stylistic context of the input audio.
3. Style Predictor: A key innovation is the diffusion-based style predictor that gleans the target expression solely from audio inputs, negating the need for additional style references. This predictor enables DreamTalk to efficiently derive personalized speaking styles, thereby streamlining the generative process and reducing external dependencies for style references.

Experimental results have showcased DreamTalk's prowess in synthesizing photorealistic talking heads with diverse speaking styles and precise lip motions. It reportedly surpasses existing state-of-the-art techniques in generating lifelike expressions, validated by a suite of quantitative metrics, including SSIM, CPBD, F-LMD, and SyncNet confidence scores.

Implications and Future Prospects

The implications of this research are manifold. DreamTalk not only advances the current methodologies in talking head generation but also opens avenues for practical applications in video games, film dubbing, and virtual avatars. By minimizing reliance on costly expression references, DreamTalk potentially reduces barriers to entry for deploying expressive avatar technologies across various domains.

Theoretically, the adaptation of diffusion models in this domain could stimulate further explorations in utilizing similar probabilistic frameworks for other generative tasks. While DreamTalk marks a significant stride, ongoing developments could explore enhancing the temporal dynamics in speaking style prediction, ensuring smoother transitions in expressions over time. Additionally, integrating robust mechanisms to counteract artifacts and ensure superior identity preservation remain promising trajectories for ongoing research.

In summary, DreamTalk positions itself as a substantial contribution to the field, harnessing diffusion models' capabilities to address the nuanced complexities in expressive talking head generation. This alignment of theoretical advancement with practical application underscores its potential impact across both academic and commercial spheres.