SurFhead: Achieving Geometrically Accurate 2D Gaussian Surfel Head Avatars Using Affine Rig Blending

Keskeiset käsitteet

SurFhead is a novel method for reconstructing highly realistic and geometrically accurate dynamic head avatars from RGB videos, leveraging 2D Gaussian surfels, affine rig blending, and improved eyeball modeling for superior performance in challenging scenarios.

Tiivistelmä

Bibliographic Information:

Lee, J., Kang, T., B¨uhler, M. C., Kim, M.-J., Hwang, S., Hyung, J., Jang, H., & Choo, J. (2024). SurFhead: Affine Rig Blending for Geometrically Accurate 2D Gaussian Surfel Head Avatars. arXiv preprint arXiv:2410.11682v1.

Research Objective:

This paper introduces SurFhead, a novel method for reconstructing high-fidelity, animatable head avatars from RGB videos, addressing the limitations of existing Gaussian primitive-based methods in capturing accurate geometry and handling complex deformations.

Methodology:

SurFhead leverages 2D Gaussian surfels with affine rigging, incorporating Jacobian deformation gradients for precise surface and normal transformations. It introduces Jacobian Blend Skinning (JBS) to smoothly interpolate deformations across adjacent mesh triangles, mitigating discontinuities. Additionally, SurFhead tackles the "hollow-eye" illusion by regularizing eyeball convexity and employing Anisotropic Spherical Gaussians (ASGs) for enhanced specularity.

Key Findings:

• SurFhead achieves state-of-the-art geometry reconstruction and rendering quality, outperforming existing methods in capturing fine details and handling extreme poses.
• Jacobian deformation gradients, coupled with JBS, significantly improve the accuracy of surface and normal reconstructions, particularly in challenging areas like the jawline and nasal region.
• The use of ASGs and eyeball regularization effectively addresses the hollow-eye illusion, resulting in more realistic eye rendering with accurate specular highlights.

Main Conclusions:

SurFhead presents a significant advancement in dynamic head avatar reconstruction, achieving a compelling balance between photorealism and geometric accuracy. Its novel techniques for deformation handling and eyeball modeling pave the way for high-fidelity avatar creation with applications in various fields, including entertainment, virtual reality, and telepresence.

Significance:

This research significantly contributes to the field of computer graphics by introducing a robust and efficient method for creating realistic and animatable head avatars from readily available RGB video data. The proposed techniques have the potential to enhance the realism and fidelity of virtual characters in various applications.

Limitations and Future Research:

While SurFhead demonstrates impressive results, limitations remain due to the reliance on 3D Morphable Face Models (3DMFMs), which have bounded expression spaces and lack detailed representations of certain facial features like the tongue and individual hair strands. Future research could explore incorporating more expressive and detailed face models or developing hybrid approaches that combine the strengths of different representations. Additionally, optimizing the computational efficiency of polar decomposition, a key component of JBS, could further enhance the method's practicality.

Tilastot

IMAvatar is approximately 200x slower than PointAvatar in training.
SurFhead amplifies the view-space gradients of the tooth by 20x during training.

Lainaukset

"In response, we introduce SurFhead, the first geometrically accurate head avatar model within the Gaussian Splatting framework (Kerbl et al., 2023), designed to capture deformation of head geometry."
"Our method is fundamentally based on 3DMFM mesh binding inheritance, similar to GaussianAvatars (Qian et al., 2024), which uses 3DMFM-based mesh triangle deformation."
"To summarize our key contributions..."

Tärkeimmät oivallukset

SurFhead: Affine Rig Blending for Geometrically Accurate 2D Gaussian Surfel Head Avatars

by Jaes... klo arxiv.org 10-16-2024

https://arxiv.org/pdf/2410.11682.pdf