VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment (2312.04651v1)

Abstract: We present a 3D-aware one-shot head reenactment method based on a fully volumetric neural disentanglement framework for source appearance and driver expressions. Our method is real-time and produces high-fidelity and view-consistent output, suitable for 3D teleconferencing systems based on holographic displays. Existing cutting-edge 3D-aware reenactment methods often use neural radiance fields or 3D meshes to produce view-consistent appearance encoding, but, at the same time, they rely on linear face models, such as 3DMM, to achieve its disentanglement with facial expressions. As a result, their reenactment results often exhibit identity leakage from the driver or have unnatural expressions. To address these problems, we propose a neural self-supervised disentanglement approach that lifts both the source image and driver video frame into a shared 3D volumetric representation based on tri-planes. This representation can then be freely manipulated with expression tri-planes extracted from the driving images and rendered from an arbitrary view using neural radiance fields. We achieve this disentanglement via self-supervised learning on a large in-the-wild video dataset. We further introduce a highly effective fine-tuning approach to improve the generalizability of the 3D lifting using the same real-world data. We demonstrate state-of-the-art performance on a wide range of datasets, and also showcase high-quality 3D-aware head reenactment on highly challenging and diverse subjects, including non-frontal head poses and complex expressions for both source and driver.

• The paper introduces a novel volumetric neural disentanglement approach that separates source appearance from driver expressions for one-shot 3D head reenactment.
• It employs a tri-plane representation with fine-tuning on real-world video data to enhance rendering fidelity and overcome the limits of linear face models.
• The method demonstrates superior performance through key metrics like PSNR, SSIM, LPIPS, and FID, paving the way for advances in AR/VR and holographic telepresence.

Volumetric Disentanglement and Real-Time 3D Head Reenactment: A Critical Analysis of VOODOO 3D

The academic paper titled "VOODOO 3D: Volumetric Portrait Disentanglement for One-Shot 3D Head Reenactment" introduces a novel methodology for 3D-aware head reenactment, leveraging a volumetric neural disentanglement framework to separate source appearance and driver expressions. This research proposes a real-time method that produces high-fidelity, view-consistent outputs, ideal for 3D teleconferencing systems, especially those employing holographic displays.

Methodological Contributions

The authors critically address the limitations of existing 3D-aware techniques that often suffer from identity leakage or unnatural expressions due to their reliance on linear face models like 3DMM. Instead, this paper proposes a neural self-supervised disentanglement approach that integrates both source and driver images into a shared 3D volumetric representation, employing tri-planes to enhance rendering fidelity. This representation allows for the manipulation of expression tri-planes derived from driving images, rendered from arbitrary viewpoints using neural radiance fields.

An intriguing component of their methodology is the fine-tuning mechanism of the 3D lifting model. Using real-world video data rather than synthetic sources, the model achieves better generalizability. This aspect underscores the model's adaptability to diverse subjects and complex expressions, overcoming the limitations of training only on synthetic datasets.

Results and Performance

The numerical experiments conducted reflect the method's superiority over current state-of-the-art techniques across various datasets, showcasing robustness in handling difficult and varied head poses and expressions. The quantitative measures such as PSNR, SSIM, LPIPS, and FID establish the method's efficacy in maintaining identity likeness and expression accuracy. Notably, the paper provides evidence of the technique's capacity to manage non-frontal views, a significant challenge in voluminous data representation and synthesis.

Practical and Theoretical Implications

Practically, VOODOO 3D, with its view-consistent neural fields and fine-scale expression synthesis, presents a transformative approach for applications in AR/VR and holographic telepresence. The technology extends possibilities for creating realistic 3D avatars from minimal input. Theoretically, this research demonstrates the efficacy of fully volumetric disentanglement models, arguing for their utility over traditional linear approaches. It highlights the potential of facial tri-plane representations in improving the fidelity of dynamic expressions in static inputs.

Future AI Directions

The paper opens avenues for future research in the refinement of volumetric representations and their application in full-body dynamics, potentially impacting virtual reality experiences significantly. Furthermore, integrating this technology with advanced generative models could enhance the photo-realism and expressiveness of avatars.

Conclusion

In conclusion, VOODOO 3D contributes significantly to the domain of neural head reenactment by providing a nuanced approach to identity and expression disentanglement, effectively circumventing the shortcomings of traditional methods. Its implications for enhancing 3D telepresence systems are substantial, encouraging deeper explorations into volumetric neural networks and their real-time applications. This research could serve as a foundational methodology for future advancements in realistic avatar creation for immersive technologies.