Relightable Full-Body Gaussian Codec Avatars (2501.14726v1)

Abstract: We propose Relightable Full-Body Gaussian Codec Avatars, a new approach for modeling relightable full-body avatars with fine-grained details including face and hands. The unique challenge for relighting full-body avatars lies in the large deformations caused by body articulation and the resulting impact on appearance caused by light transport. Changes in body pose can dramatically change the orientation of body surfaces with respect to lights, resulting in both local appearance changes due to changes in local light transport functions, as well as non-local changes due to occlusion between body parts. To address this, we decompose the light transport into local and non-local effects. Local appearance changes are modeled using learnable zonal harmonics for diffuse radiance transfer. Unlike spherical harmonics, zonal harmonics are highly efficient to rotate under articulation. This allows us to learn diffuse radiance transfer in a local coordinate frame, which disentangles the local radiance transfer from the articulation of the body. To account for non-local appearance changes, we introduce a shadow network that predicts shadows given precomputed incoming irradiance on a base mesh. This facilitates the learning of non-local shadowing between the body parts. Finally, we use a deferred shading approach to model specular radiance transfer and better capture reflections and highlights such as eye glints. We demonstrate that our approach successfully models both the local and non-local light transport required for relightable full-body avatars, with a superior generalization ability under novel illumination conditions and unseen poses.

• The paper's main contribution is the innovative decomposition of light transport into local and non-local components using efficient zonal harmonics for full-body avatar simulation.
• It introduces a shadow network integration to model occlusion-driven shadows, accurately capturing nuanced lighting effects under diverse poses.
• The approach employs deferred shading for specular radiance, ensuring high-fidelity visuals and dynamic detail preservation in varying illumination conditions.

Relightable Full-Body Gaussian Codec Avatars: A Comprehensive Review

This paper introduces a sophisticated approach to the modeling of relightable full-body avatars, titled "Relightable Full-Body Gaussian Codec Avatars." The central challenge addressed by this research lies in effectively capturing and simulating the intricate details presented by full-body articulations, such as face and hand movements, especially under varying illumination conditions. This is achieved by implementing a novel method that decomposes the light transport into local and non-local contributions, facilitated through Gaussian-based representations.

Fundamental Concepts and Advances

The approach is grounded in the decomposition of light transport into local and non-local phenomena, allowing for detailed simulations of shadows and radiance effects. This is a considerable advancement over traditional methods that struggle to capture the nuanced interactions of light due to complex body poses and concurrent occlusions. Crucially, the research leverages learnable zonal harmonics for modeling diffuse radiance transfer in local coordinate frames, which enhances efficiency and accuracy in representing light interaction with various body surfaces under articulation. Such representation enables accurate rotation of the harmonics in alignment with the world's coordinate frames, a task that spherical harmonics typically grapple with due to computational complexity.

Methodological Depth

Key contributions of this work include:

1. Zonal Harmonics Efficiency: The paper introduces zonal harmonics as an alternative to spherical harmonics, allowing for a compact and computationally efficient model of light transport. Zonal harmonics are advantageous because they can be adjusted for rotations much more efficiently than spherical harmonics, especially when dealing with high-frequency lighting environments that are affected by articulation in different body segments.
2. Shadow Network Integration: To handle non-local shadowing caused by body part occlusion, a shadow predictive network is employed. This network is tasked with modeling such shadows based on precomputed incoming irradiance on a simplified base mesh, thus offering a robust framework that adapts to new lighting scenarios beyond the training conditions.
3. Deferred Shading for Specular Radiance: Another key aspect is the adoption of a deferred shading approach for specular radiance, which accurately captures reflections and highlights such as eye glints. This model opposes previous simplifications in literature by directly rendering and preserving high-frequency details, critical for applications requiring high-fidelity visuals, such as in gaming and virtual reality.

Comparative Insights and Implications

Quantitative evaluations against baseline models highlight the superiority of this technique, particularly in nuanced scenarios involving unseen body poses and unprecedented lighting conditions. The innovative employment of zonal harmonics alongside a dedicated shadow network allows the model to perform at a high level of accuracy, overcoming limitations typically present in mesh-based or ray-traced systems.

Future Perspectives in AI

The implications of this research are significant for future developments in AI-driven avatar creation, particularly for applications involving real-time rendering or dynamic environments. By offering a robust framework to capture and simulate realistic human avatars under any light conditions, the paper paves the way for enhancements in interactive media, including more immersive augmented and virtual reality experiences.

In summary, this work provides a substantial leap forward in avatar technology, emphasizing efficiency, accuracy, and realism in capturing the complex interactions between articulated human bodies and light. Future research could potentially expand on these foundations by integrating more diverse environmental variables, improving scalability, and exploring new domains of AI convergence with graphics, such as telepresence and digital twin applications.