Gaussian Eigen Models for Human Heads (2407.04545v4)

Abstract: Current personalized neural head avatars face a trade-off: lightweight models lack detail and realism, while high-quality, animatable avatars require significant computational resources, making them unsuitable for commodity devices. To address this gap, we introduce Gaussian Eigen Models (GEM), which provide high-quality, lightweight, and easily controllable head avatars. GEM utilizes 3D Gaussian primitives for representing the appearance combined with Gaussian splatting for rendering. Building on the success of mesh-based 3D morphable face models (3DMM), we define GEM as an ensemble of linear eigenbases for representing the head appearance of a specific subject. In particular, we construct linear bases to represent the position, scale, rotation, and opacity of the 3D Gaussians. This allows us to efficiently generate Gaussian primitives of a specific head shape by a linear combination of the basis vectors, only requiring a low-dimensional parameter vector that contains the respective coefficients. We propose to construct these linear bases (GEM) by distilling high-quality compute-intense CNN-based Gaussian avatar models that can generate expression-dependent appearance changes like wrinkles. These high-quality models are trained on multi-view videos of a subject and are distilled using a series of principal component analyses. Once we have obtained the bases that represent the animatable appearance space of a specific human, we learn a regressor that takes a single RGB image as input and predicts the low-dimensional parameter vector that corresponds to the shown facial expression. In a series of experiments, we compare GEM's self-reenactment and cross-person reenactment results to state-of-the-art 3D avatar methods, demonstrating GEM's higher visual quality and better generalization to new expressions.

• 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.