HeadGAP: Few-Shot 3D Head Avatar via Generalizable Gaussian Priors (2408.06019v2)

Abstract: In this paper, we present a novel 3D head avatar creation approach capable of generalizing from few-shot in-the-wild data with high-fidelity and animatable robustness. Given the underconstrained nature of this problem, incorporating prior knowledge is essential. Therefore, we propose a framework comprising prior learning and avatar creation phases. The prior learning phase leverages 3D head priors derived from a large-scale multi-view dynamic dataset, and the avatar creation phase applies these priors for few-shot personalization. Our approach effectively captures these priors by utilizing a Gaussian Splatting-based auto-decoder network with part-based dynamic modeling. Our method employs identity-shared encoding with personalized latent codes for individual identities to learn the attributes of Gaussian primitives. During the avatar creation phase, we achieve fast head avatar personalization by leveraging inversion and fine-tuning strategies. Extensive experiments demonstrate that our model effectively exploits head priors and successfully generalizes them to few-shot personalization, achieving photo-realistic rendering quality, multi-view consistency, and stable animation.

• The paper presents a two-stage framework leveraging generalizable 3D Gaussian priors to achieve effective few-shot personalization of high-fidelity head avatars.
• The methodology employs a prior learning phase with GAPNet and inversion-based fine-tuning, yielding state-of-the-art metrics such as LPIPS, PSNR, and SSIM.
• Experimental results show significant enhancements in rendering quality and multi-view consistency, paving the way for applications in AR/VR, gaming, and digital media.

HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors

The paper introduces HeadGAP, a pioneering approach focused on creating high-fidelity 3D head avatars using minimal input data. The primary goal is to address the challenges in personalized 3D avatar creation, particularly when available data is sparse. HeadGAP achieves this by leveraging generalizable 3D Gaussian priors extracted from extensive 3D head data during the prior learning phase. Notably, the proposed framework effectively exploits these priors for few-shot personalization by employing inversion and fine-tuning mechanisms.

Methodology

HeadGAP Framework:

The HeadGAP framework is divided into two crucial phases: prior learning and few-shot personalization. This dual-phase approach ensures that the model is well-equipped to handle the inherent underconstrained nature of the problem.

1. Prior Learning Phase:
   • In this phase, the model learns 3D Gaussian priors using a large-scale multi-view dynamic dataset.
   • The core of this phase is the GAPNet (GAussian Prior Network), which utilizes a Gaussian Splatting-based auto-decoder network. This network is enriched with part-based dynamic modeling to enhance its prior learning capacity.
   • Identity-shared encoding is employed alongside personalized latent codes, enabling the network to comprehensively learn the attributes of Gaussian primitives.
2. Few-shot Personalization Phase:
   • This phase applies the learned priors for few-shot head avatar creation. The model personalizes the head avatars through inversion and fine-tuning strategies, ensuring fast adaptation to new identities.
   • The inversion process finds the most similar identity code from the learned priors, followed by fine-tuning that adjusts the network parameters to capture the nuances of the few available images of the target identity.

Experimental Results

The efficacy of HeadGAP is demonstrated through extensive experiments on the NeRSemble dataset. Key metrics such as LPIPS, PSNR, SSIM, and ID similarity are utilized to evaluate the performance. HeadGAP consistently outperforms state-of-the-art methods across these metrics, indicating its superior capability in producing photo-realistic, multi-view consistent, and animatable 3D head avatars.

• One-shot Personalization:

HeadGAP exhibits robustness even when limited to a single input image, achieving state-of-the-art performance in LPIPS, PSNR, and SSIM metrics.

• Few-shot Personalization:

When using three input images, HeadGAP achieves significant improvements in rendering quality, multi-view consistency, and identity preservation over existing methods.

Implications and Future Work

Practical Implications:

The proposed method drastically reduces the data dependency typically required for high-fidelity 3D avatar creation. This reduction opens new possibilities for applications in AR/VR, gaming, and digital media, where user-generated content and personalized avatars are increasingly prevalent.

Theoretical Implications:

By integrating part-based dynamic modeling and identity-shared encoding, HeadGAP sets a new benchmark for how priors can be effectively learned and generalized. This methodology could inspire future research in generative modeling and few-shot learning paradigms.

Future Directions:

1. Enhanced Generalization:
   • Incorporating diverse datasets, including those with varied lighting conditions and facial accessories, could further bolster the model's robustness.
2. Broader Application:
   • Extending the framework to other body parts or full-body avatars, as well as adapting it for different modalities (e.g., speech-driven animation), could provide a comprehensive solution for 3D avatar creation.
3. Real-time Performance:
   • Optimizing the computational efficiency to enable real-time avatar creation on consumer-grade hardware can significantly enhance user experience in interactive applications.

Conclusion

HeadGAP marks a significant stride in the domain of 3D head avatar creation, demonstrating that high-fidelity, personalized avatars can be achieved with minimal data inputs through effective utilization of generalizable Gaussian priors. The robust results and potential for wide-ranging applications make HeadGAP a notable contribution to computer graphics and artificial intelligence research.