Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait (2503.12963v1)

Abstract: Audio-driven single-image talking portrait generation plays a crucial role in virtual reality, digital human creation, and filmmaking. Existing approaches are generally categorized into keypoint-based and image-based methods. Keypoint-based methods effectively preserve character identity but struggle to capture fine facial details due to the fixed points limitation of the 3D Morphable Model. Moreover, traditional generative networks face challenges in establishing causality between audio and keypoints on limited datasets, resulting in low pose diversity. In contrast, image-based approaches produce high-quality portraits with diverse details using the diffusion network but incur identity distortion and expensive computational costs. In this work, we propose KDTalker, the first framework to combine unsupervised implicit 3D keypoint with a spatiotemporal diffusion model. Leveraging unsupervised implicit 3D keypoints, KDTalker adapts facial information densities, allowing the diffusion process to model diverse head poses and capture fine facial details flexibly. The custom-designed spatiotemporal attention mechanism ensures accurate lip synchronization, producing temporally consistent, high-quality animations while enhancing computational efficiency. Experimental results demonstrate that KDTalker achieves state-of-the-art performance regarding lip synchronization accuracy, head pose diversity, and execution efficiency.Our codes are available at https://github.com/chaolongy/KDTalker.

Unlock Pose Diversity: Accurate and Efficient Implicit Keypoint-based Spatiotemporal Diffusion for Audio-driven Talking Portrait

The paper introduces KDTalker, an innovative framework for generating talking portraits driven by audio input. This work addresses the challenges faced by existing methodologies in this domain, which are typically divided into keypoint-based approaches and image-based approaches. Specifically, it combines unsupervised implicit 3D keypoint techniques with spatiotemporal diffusion models to produce talking head animations that are not only temporally consistent but also computationally efficient.

Key Contributions

KDTalker Framework: The paper proposes a novel method that leverages unsupervised implicit 3D keypoints for facial animation, moving away from the rigidity of fixed-point methods, such as those using the 3D Morphable Model (3DMM). This flexibility ensures better adaptation to facial dynamics and varying expression details, overcomeing the limitations inherent in traditional keypoint-based techniques.

Spatiotemporal Diffusion Model: This model is equipped with a custom-designed spatiotemporal attention mechanism to ensure accurate lip synchronization and pose diversity, allowing facial animations to preserve character identity while enhancing computational efficiency. The innovative use of keypoint-based spatiotemporal diffusion facilitates long-range audio and keypoint dependencies, aiding in natural and precise synchronization with audiovisual cues.

Experimental Validation: The KDTalker framework achieves superior performance in experimental settings, demonstrating state-of-the-art results in metrics such as lip synchronization accuracy, head pose diversity, and computational efficiency. In particular, the method's efficient performance allows for real-time applications, which is crucial for practical integration into virtual reality and digital content creation scenarios.

Implications and Future Directions

Theoretical Impact: By challenging the conventional reliance on fixed keypoints and traditional generative networks like VAEs and GANs, this work opens up new avenues for research in facial animation and synthetic media creation. It prompts a reconsideration of how facial dynamics are modeled with respect to temporal audio cues, potentially influencing future work across animation and deepfake research.

Practical Application: For industries involved in filmmaking, virtual environments, and digital human creation, this framework offers a more efficient and versatile method for producing realistic talking head animations, with implications for avatar creation and digital conferencing technology. The reduced computational demands suggest increased accessibility for smaller teams and applications outside the commercial sector.

Future Developments in AI: As AI models continue to evolve, methodologies like those proposed in KDTalker reinforce the importance of spatiotemporal understanding in multimedia synthesis. Future models might explore hybrid approaches that integrate even more nuanced emotional and environmental contexts, driven by advancements in audio-visual correlation understanding and attention mechanisms.

This contribution offers valuable insights that inform the evolving scope of AI-driven portrait animation, emphasizing the balance between high fidelity expression and computational efficiency. KDTalker exemplifies a tangible advance in this area, and its findings will likely inspire subsequent innovations in the field.