GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction (2407.15070v2)

Abstract: Creating high-fidelity 3D human head avatars is crucial for applications in VR/AR, digital human, and film production. Recent advances have leveraged morphable face models to generate animated head avatars from easily accessible data, representing varying identities and expressions within a low-dimensional parametric space. However, existing methods often struggle with modeling complex appearance details, e.g., hairstyles, and suffer from low rendering quality and efficiency. In this paper we introduce a novel approach, 3D Gaussian Parametric Head Model, which employs 3D Gaussians to accurately represent the complexities of the human head, allowing precise control over both identity and expression. The Gaussian model can handle intricate details, enabling realistic representations of varying appearances and complex expressions. Furthermore, we presents a well-designed training framework to ensure smooth convergence, providing a robust guarantee for learning the rich content. Our method achieves high-quality, photo-realistic rendering with real-time efficiency, making it a valuable contribution to the field of parametric head models. Finally, we apply the 3D Gaussian Parametric Head Model to monocular video or few-shot head avatar reconstruction tasks, which enables instant reconstruction of high-quality 3D head avatars even when input data is extremely limited, surpassing previous methods in terms of reconstruction quality and training speed.

• The paper introduces a 3D Gaussian Parametric Head Model that achieves photorealistic, efficient 3D avatar reconstruction from monocular videos.
• It employs a two-stage training strategy, starting with an SDF-based geometry model and transitioning to a Gaussian representation for robust convergence.
• The method effectively disentangles identity and expression features, outperforming previous approaches on metrics like PSNR and LPIPS.

An Expert Review of "GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction"

The paper "GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction" by Yuelang Xu et al. presents a comprehensive solution to creating high-fidelity 3D human head avatars with a focus on real-time efficiency and accuracy even from limited data sources, such as monocular videos. This research proposes a novel 3D Gaussian parametric head model that excels over previous methodologies by achieving photorealistic rendering and providing robust convergence through innovative training strategies.

The central innovation within this work is the utilization of a 3D Gaussian-based representation, referred to as the 3D Gaussian Parametric Head Model (GPHM). This model leverages explicit Gaussian ellipsoids, offering fine control over details such as identity and expressions, which traditional methods involving morphable models or implicit Signed Distance Fields (SDF) struggled to effectively capture.

Key Contributions

1. 3D Gaussian Parametric Head Model: Unlike prior NeRF-based models which are computationally intensive and less efficient, the GPHM uses Gaussian splats for representation, resulting in high-quality, photorealistic outputs while maintaining rendering efficiency.
2. Training Strategy: A two-stage training process was devised that first involves training a guiding geometry model based on signed distance fields, followed by a migration to the Gaussian model. This mitigated convergence issues typically arising from the unstructured nature of Gaussian ellipsoids. Moreover, the use of pre-computed multi-view video data and synthetic datasets enhances the robustness of the model against limited data scenarios.
3. Disentanglement of Identity and Expression: Through carefully structured latent spaces and network design, the authors manage to seamlessly decouple identity information from expressions, allowing for precise avatar manipulation and animation. This characteristic marks a departure from traditional 3DMM-based approaches where such parameters were inherently coupled, often resulting in suboptimal cross-identity application performance.
4. Applications and Performance: The results demonstrate that GPHM can not only reconstruct detailed 3D head avatars from sparse input data but also support cross-identity reenactment with superior performance metrics such as PSNR and LPIPS compared to state-of-the-art methods. This capability represents a significant improvement for applications in VR/AR, film production, and telepresence.
5. Broad Dataset Utilization: The research utilizes several datasets, including both real and synthetic 3D scans, showcasing the versatility of the method in learning across varied types of input data, thus enhancing its applicability and generalization.

Implications and Future Directions

Practically, the outcomes of this research stand to significantly advance the state of avatar creation in applications that demand realistic personal representations from minimal input resources, such as interactive VR systems or digital content creation studios. The improvement in speed and quality of rendering delivered by 3D Gaussian models could see widespread tool adoption among developers and content creators who require scalable, high-fidelity human representations.

Theoretically, this work opens avenues for further exploration in Gaussian-based representations within domain-specific generative tasks, potentially reshaping how deformation and appearance modeling are approached in dynamic systems. Future research could expand by integrating novel AI-driven refinement techniques or by broadening the applicability of Gaussian representations in other human modeling tasks, including full-body reconstruction or dynamic gesture synthesis.

This paper exemplifies a strong contribution to the field of computer graphics and vision, paving the way for future explorations in efficient, accurate, and scalable 3D modeling practices.