SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis (2311.17590v2)

Abstract: Achieving high synchronization in the synthesis of realistic, speech-driven talking head videos presents a significant challenge. Traditional Generative Adversarial Networks (GAN) struggle to maintain consistent facial identity, while Neural Radiance Fields (NeRF) methods, although they can address this issue, often produce mismatched lip movements, inadequate facial expressions, and unstable head poses. A lifelike talking head requires synchronized coordination of subject identity, lip movements, facial expressions, and head poses. The absence of these synchronizations is a fundamental flaw, leading to unrealistic and artificial outcomes. To address the critical issue of synchronization, identified as the "devil" in creating realistic talking heads, we introduce SyncTalk. This NeRF-based method effectively maintains subject identity, enhancing synchronization and realism in talking head synthesis. SyncTalk employs a Face-Sync Controller to align lip movements with speech and innovatively uses a 3D facial blendshape model to capture accurate facial expressions. Our Head-Sync Stabilizer optimizes head poses, achieving more natural head movements. The Portrait-Sync Generator restores hair details and blends the generated head with the torso for a seamless visual experience. Extensive experiments and user studies demonstrate that SyncTalk outperforms state-of-the-art methods in synchronization and realism. We recommend watching the supplementary video: https://ziqiaopeng.github.io/synctalk

• The paper introduces a tri-module NeRF-based framework that precisely synchronizes lip movement, head pose, and facial expressions.
• It leverages a Face-Sync Controller, Head-Sync Stabilizer, and Portrait-Sync Generator to overcome limitations of traditional GAN and NeRF methods.
• Experimental evaluations demonstrate improved image quality metrics and real-time performance up to 50 FPS, setting a new benchmark in talking head synthesis.

SyncTalk: Addressing Synchronization Challenges in Talking Head Synthesis

The paper "SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis" presents a novel NeRF-based approach designed to create realistic, speech-driven talking head videos. SyncTalk addresses core synchronization challenges inherent to Generative Adversarial Networks (GAN) and Neural Radiance Fields (NeRF) in synthesizing talking heads. It excels in maintaining subject identity and synchronizing lip movements, facial expressions, and head poses, thereby enhancing the realism of synthetic videos to meet human perceptual expectations.

Key Contributions

SyncTalk introduces a comprehensive framework, tackling long-standing issues in talking head synthesis through three primary components:

1. Face-Sync Controller: This module ensures lip synchronization by employing a pre-trained audio-visual encoder built on a large 2D audio-visual dataset. It strategically utilizes a 3D facial blendshape model with 52 control parameters for facial expressions, offering a semantically rich mechanism for precise facial animation capture.
2. Head-Sync Stabilizer: To mitigate instability in head poses observed in previous methods, SyncTalk uses a two-stage optimization process. Initially, rough pose estimates are refined using a head motion tracker. Further optimization through a bundle adjustment using dense keypoints results in synchronized and stable head motion.
3. Portrait-Sync Generator: Complementing the facial synthesis, this module enhances visual quality by refining NeRF artifacts, restoring intricate hair details, and ensuring seamless integration of the generated head with the body.

Methodological Insight

SyncTalk's methodology uniquely addresses the gap between intricate facial details and synchronization. While traditional GANs struggle with maintaining consistent facial identity, NeRF methods often face mismatches in lip movements and unstable head poses. SyncTalk circumvents these limitations with its tri-plane hash representation to preserve identity consistency and detail fidelity. The integration of various synchronization modules ensures the generated head movements and expressions align naturally with the corresponding audio inputs.

Experimental Validation

The paper provides a thorough evaluation against several state-of-the-art GAN and NeRF-based methods. SyncTalk demonstrates superior performance across various metrics, including PSNR, LPIPS, MS-SSIM, and FID. Moreover, its speed achieves real-time rendering capabilities (up to 50 FPS), underscoring its efficiency alongside accuracy. Notably, its performance enhancement is marked by a threefold improvement in LPIPS compared to preceding methods, highlighting substantial advancements in image quality and synchronization.

Implications and Future Directions

The implications of SyncTalk's achievements are substantial, especially in applications like digital assistants, virtual reality, and film-making. By achieving high synchronization levels, it paves the way for more genuine and immersive virtual interactions. Future research may further explore refinement in dynamic backgrounds and enhanced generalization to diverse audio inputs, potentially involving larger and more varied datasets to bolster the model's adaptability.

Moreover, considering the ethical concerns surrounding the potential misuse of generated content, it is crucial for the community to concurrently advance detection tools to mitigate risks associated with synthetic media.

In conclusion, SyncTalk represents a significant advancement in synthesizing talking heads, addressing synchronization challenges with precision and efficacy. Its contribution sets a new benchmark in the field, offering a robust framework for future explorations in realistic human-computer interaction.