Gaussian Eigen Models for Human Heads (2407.04545v1)

Abstract: We present personalized Gaussian Eigen Models (GEMs) for human heads, a novel method that compresses dynamic 3D Gaussians into low-dimensional linear spaces. Our approach is inspired by the seminal work of Blanz and Vetter, where a mesh-based 3D morphable model (3DMM) is constructed from registered meshes. Based on dynamic 3D Gaussians, we create a lower-dimensional representation of primitives that applies to most 3DGS head avatars. Specifically, we propose a universal method to distill the appearance of a mesh-controlled UNet Gaussian avatar using an ensemble of linear eigenbasis. We replace heavy CNN-based architectures with a single linear layer improving speed and enabling a range of real-time downstream applications. To create a particular facial expression, one simply needs to perform a dot product between the eigen coefficients and the distilled basis. This efficient method removes the requirement for an input mesh during testing, enhancing simplicity and speed in expression generation. This process is highly efficient and supports real-time rendering on everyday devices, leveraging the effectiveness of standard Gaussian Splatting. In addition, we demonstrate how the GEM can be controlled using a ResNet-based regression architecture. We show and compare self-reenactment and cross-person reenactment to state-of-the-art 3D avatar methods, demonstrating higher quality and better control. A real-time demo showcases the applicability of the GEM representation.

• The paper presents a novel GEM method that condenses dynamic 3D Gaussian representations into a low-dimensional linear space for rapid, real-time avatar rendering.
• It replaces computationally heavy CNNs with streamlined linear layers, yielding superior PSNR, SSIM, and LPIPS scores compared to existing techniques.
• The framework enables personalized avatar creation and cross-person reenactment, offering significant advancements for virtual communication and digital content applications.

Gaussian Eigen Models for Human Heads: A Precision Statistical Representation for Efficient 3D Avatar Rendering

The paper "Gaussian Eigen Models for Human Heads" by Zielonka et al. introduces an innovative method for the creation and manipulation of 3D human head models using Gaussian Eigen Models (GEMs). This approach leverages the efficiency of Gaussian distributions to create highly detailed, photo-realistic avatars with reduced computational overhead compared to existing methods. Rooted in mesh and neural-based modeling techniques, the paper presents a significant advancement in the efficient representation and manipulation of dynamic facial expressions.

The methodology hinges on distilling dynamic 3D Gaussian representations into a low-dimensional linear space, inspired by the 3D morphable models (3DMMs) established by Blanz and Vetter. The authors propose a technique that replaces computationally heavy CNN architectures with linear layers, thereby improving real-time application capabilities. In essence, the GEM utilizes a sequence of 3D Gaussian primitives, capturing variance in facial expressions through a linear blend of Gaussian coefficients. By converting video-captured facial data into a highly compressed linear representation, GEMs facilitate fast and storage-efficient rendering, suitable for real-time applications on common devices.

A distinguishing feature of this method is its universality and efficiency. Unlike many prior techniques, GEMs do not necessitate the use of a specific 3D morphable model, such as the FLAME model. Instead, they provide a powerful, compact eigenbasis that can translate nuanced facial expressions from sparse data sets into fully-realized 3D avatars. Furthermore, this eigenbasis operates independently from the complex mesh input normally required, using Gaussian maps to replace direct mesh manipulation during rendering.

The results showcased, including reconstructions of facial wrinkles and complex expressions absent in the training set, demonstrate superior performance over comparable state-of-the-art methods like Gaussian Avatars and Animatable Gaussians. The GEM approach achieves improved PSNR, SSIM, and LPIPS scores, indicating higher fidelity in reconstructed appearances, enhanced detail retention, and minimal perceptual difference from ground truth video frames.

Beyond its computational efficiency, the GEM’s linear model allows for applications in personalized avatar creation, enabling the transfer of captured expressions to other users through cross-person reenactment. This highlights practical implications in areas such as virtual communication, gaming, and digital content creation, where real-time adaptability and low resource consumption are critical.

Speculatively, the methods outlined in this paper could influence future developments in AI-driven avatar systems, particularly in refining how statistical models interface with graphical rendering processes. Potential directions for continued research might explore expanding GEMs to full-body avatars or integrating with machine learning frameworks for enhanced synthesis from audio or textual prompts. Additionally, the GEM framework's applicability could be broadened to include novel volumetric and geometric primitives beyond Gaussians.

In conclusion, Gaussian Eigen Models offer a promising avenue for the efficient, high-quality synthesis of human head avatars. By innovatively fusing statistical analysis with practical rendering techniques, this paper makes a notable contribution to the field of 3D modeling and rendering, setting a foundation for future explorations into streamlined avatar representations and real-time digital character animation.