PAV: Personalized Head Avatar from Unstructured Video Collection (2407.21047v1)

Abstract: We propose PAV, Personalized Head Avatar for the synthesis of human faces under arbitrary viewpoints and facial expressions. PAV introduces a method that learns a dynamic deformable neural radiance field (NeRF), in particular from a collection of monocular talking face videos of the same character under various appearance and shape changes. Unlike existing head NeRF methods that are limited to modeling such input videos on a per-appearance basis, our method allows for learning multi-appearance NeRFs, introducing appearance embedding for each input video via learnable latent neural features attached to the underlying geometry. Furthermore, the proposed appearance-conditioned density formulation facilitates the shape variation of the character, such as facial hair and soft tissues, in the radiance field prediction. To the best of our knowledge, our approach is the first dynamic deformable NeRF framework to model appearance and shape variations in a single unified network for multi-appearances of the same subject. We demonstrate experimentally that PAV outperforms the baseline method in terms of visual rendering quality in our quantitative and qualitative studies on various subjects.

• The paper presents a unified neural framework that integrates varied facial appearances into a single deformable NeRF model.
• It leverages vertex-attached latent features to synthesize high-fidelity textures and realistic facial geometry from unstructured video data.
• Empirical results demonstrate superior rendering quality, capturing dynamic expressions and identity-specific nuances better than traditional methods.

Analysis of PAV: Personalized Head Avatar from Unstructured Video Collection

The paper "PAV: Personalized Head Avatar from Unstructured Video Collection" presents a sophisticated framework dedicated to advancing the generation of personalized head avatars. This work is particularly innovative in the context of utilizing unstructured monocular talking face video collections to generate dynamic, deformable Neural Radiance Fields (NeRFs). Focusing on discrete individuals across different temporal appearances, PAV offers a cohesive solution for creating avatars that are not only representative of facial and geometric variances but are also asset-light with respect to training data prerequisites.

Overview of Approach

Central to PAV's methodology is the employment of a dynamic deformable NeRF, which diverges from conventional per-appearance modeling. Traditional methods often require on-demand training for each separate appearance, which is both computationally intensive and impractical for real-world applications involving multimedia content spanning diverse conditions and epochs. PAV, on the other hand, introduces a profound optimization technique that captures both shape and appearance variations in one holistic framework. The model leverages learnable latent neural features anchored to geometry and a shared volumetric representation that spans multiple observed facial states of the subject.

Technical Contributions

1. Unified Network Architecture: PAV pioneers the amalgamation of varied appearances into a single neural model, effectively synthesizing density and radiance conditioned on both geometric deformations and appearance embeddings. This comprehensive framework significantly advances the utility of NeRFs by streamlining the integration of multi-appearance data.
2. Appearance-Conditioned Synthesis: Leveraging vertex-attached latent features, PAV facilitates superior quality rendering by embedding appearance-specific attributes directly into the radiance field. This approach offers advantages in resolving texture fidelity and accurate geometry representation, which are pivotal in achieving realism.
3. Empirical Validation: The authors validate their approach using a custom dataset that depicts multiple facial variations across several subjects, demonstrating that PAV outperforms existing methods in terms of visual rendering quality. The experimental results underscore the model’s capacity to maintain coherent expressions and identity-specific nuances across distinct appearances, thereby pushing the envelope in avatar synthesis fidelity.

Implications and Future Directions

From a theoretical standpoint, PAV's integration of dynamic deformable NeRFs represents a leap toward the efficient deployment of neural avatars in various sectors such as animation, gaming, and telepresence. The reduced need for isolated per-appearance optimizations paves the way for broader applications, particularly in real-time environments and applications reliant on rapid avatar customization.

Practically, this technology underscores the potential for more accessible and versatile digital persona representations, with implications for both content creators and consumers seeking personalized experiences. The reduction in computational overhead offers additional avenues for scalability, potentially unlocking mass personalization capacity without proportional increases in resource requirements.

Looking forward, challenges such as multi-identity integration and the ethical implications of neural avatar technology merit further investigation. Although the paper acknowledges these aspects, achieving practical solutions will be critical to preventing misuse and ensuring that neural avatar advancements provide unmitigated societal benefit.

In conclusion, the PAV framework stands as a significant contribution to the field of computer vision and AI, particularly in its methodical handling of unstructured data for dynamic head avatar generation. As more sophisticated models and datasets emerge, expanding techniques like PAV could herald a new era in personalized virtual identity formation, with a cascading impact on digital interaction paradigms.