Researchers at the University of Wisconsin and ByteDance present PanoHead: the first 3D GAN framework that synthesizes view-consistent full-head images with only single-view images

In computer vision and graphics, photorealistic portrait image synthesis has been consistently emphasized, with a wide range of subsequent applications in virtual avatars, telepresence, immersive gaming, and many other areas. Indistinguishable from genuine images, recent developments in Generative Adversarial Networks (GANs) have shown remarkably high image synthesis quality. However, contemporary generative methods do not model the underlying 3D scenes; instead, they operate on 2D convolutional networks. As a result, it is impossible to adequately guarantee 3D consistency when synthesizing images of heads in different positions. Traditional methods require a parametric textured mesh model learned from extensive collections of 3D scans to produce 3D heads with various shapes and appearances.

The images produced, however, need finer details and have little expressiveness and perceptual quality. To make more realistic 3D images of faces, conditional generative models have been created with the advent of differentiable representation and implicit neural representation. These methods, however, often rely on multi-view image or 3D scan monitoring, which is difficult to obtain and has a restricted appearance distribution because it is typically recorded in controlled environments. Recent developments in implicit neural representation in 3D scene modeling and generative antagonistic networks (GANs) for image synthesis have accelerated the development of 3D-compatible generative models.

One of these, the pioneering 3D GAN, EG3D, has impressive quality in consistent view image synthesis and was trained using single-view image sets found in nature. However, these 3D GAN methods can only synthesize in near frontal perspectives. Researchers at ByteDance and the University of Wisconsin-Madison suggest PanoHead, a unique 3D-aware GAN trained using only unstructured photos in the wild, enabling full 360 high-quality 3D head synthesis. Numerous interaction scenarios Immersive, including telepresence and digital avatars, benefit from your model’s ability to synthesize consistent 3D heads that can be viewed from all perspectives. They believe their methodology is the first 3D GAN approach to perform full 360 degree 3D head synthesis.

There are several significant technological obstacles to full 3D head synthesis when using 3D GAN frameworks such as EG3D: Many 3D GANs cannot distinguish between foreground and background, leading to 2.5D head geometry. . Large poses cannot be rendered because the background, normally structured as a wall structure, becomes entangled with the 3D created head. They develop a foreground-aware tridiscriminator that, using prior information from 2D image segmentation, simultaneously learns the decomposition of the foreground head in 3D space. Additionally, hybrid 3D scene renderings such as the triplane offer significant projection uncertainty for 360-degree camera poses, resulting in a “reflected face” in the rear head despite its efficiency. and compact size.

They provide a unique 3D tri-grid volume rendering that separates frontal features from the rear head while retaining the power of three-plane renderings to address the problem. Finally, obtaining accurate camera extrinsic rear head images in the wild for 3D GAN training is quite challenging. Additionally, there is a discrepancy in image alignment between these and frontal photos with discernible facial landmarks. Unattractive head geometry and a noisy appearance result from the alignment gap. As a result, they suggest a unique two-stage alignment method that reliably aligns photos from all perspectives. This procedure considerably reduces the learning curve of 3D GANs.

They specifically suggest a camera autoadaptation module that dynamically modifies render camera locations to account for alignment deviations in rear head images. As seen in Figure 1, their approach significantly improves the ability of 3D GANs to acclimate to full head shots in the wild from arbitrary vantage points. The resulting 3D GAN creates high-fidelity 360° RGB geometry and images and surpasses state-of-the-art techniques in quantitative measurements. With this model, they demonstrate how to create a 3D portrait with ease by rebuilding a full 3D head from a single monocular shot.

The following is a summary of his main contributions:

• The first 3D GAN framework capable of generating 360-degree full-head image synthesis that is eye-consistent and high-fidelity. They use a high-quality monocular 3D head reconstruction from photographs taken in the field to illustrate their methodology.

• A unique triple grid formulation for voicing 360 degree 3D headstages that compromises effectiveness and expressiveness.

• A tri-discriminator that separates 2D background synthesis from 3D foreground head modeling.

• A state-of-the-art two-stage image alignment technique that adaptively adapts to poor camera postures and misaligned image cropping, enabling 3D GAN to be trained from nature shots with a wide gamut. of camera poses.

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