PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^{\circ}$ (2303.13071v1)

Abstract: Synthesis and reconstruction of 3D human head has gained increasing interests in computer vision and computer graphics recently. Existing state-of-the-art 3D generative adversarial networks (GANs) for 3D human head synthesis are either limited to near-frontal views or hard to preserve 3D consistency in large view angles. We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360^\circ$ with diverse appearance and detailed geometry using only in-the-wild unstructured images for training. At its core, we lift up the representation power of recent 3D GANs and bridge the data alignment gap when training from in-the-wild images with widely distributed views. Specifically, we propose a novel two-stage self-adaptive image alignment for robust 3D GAN training. We further introduce a tri-grid neural volume representation that effectively addresses front-face and back-head feature entanglement rooted in the widely-adopted tri-plane formulation. Our method instills prior knowledge of 2D image segmentation in adversarial learning of 3D neural scene structures, enabling compositable head synthesis in diverse backgrounds. Benefiting from these designs, our method significantly outperforms previous 3D GANs, generating high-quality 3D heads with accurate geometry and diverse appearances, even with long wavy and afro hairstyles, renderable from arbitrary poses. Furthermore, we show that our system can reconstruct full 3D heads from single input images for personalized realistic 3D avatars.

• The paper introduces a novel GAN architecture that leverages a tri-grid neural volume representation to generate view-consistent 3D head models from single-view inputs.
• It employs a two-stage self-adaptive image alignment and a foreground-aware tri-discriminator to reduce noise and effectively preserve identity details.
• Empirical evaluations show significant improvements in fidelity, lower segmentation error, and robust handling of diverse camera poses compared to prior methods.

PanoHead: Advancements in Full-Head 3D Synthesis with Geometry-Aware GANs

The paper "PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^\circ$" presents a pioneering approach in the domain of computer vision and graphics, focusing on the generation of full 3D head models with view-consistent realism across the entire 360-degree panorama. This work is particularly noteworthy for its reliance solely on unstructured, single-view image inputs for training, thereby addressing a significant gap in the current capabilities of 3D Generative Adversarial Networks (GANs).

Core Contributions and Methodology

PanoHead introduces a novel architecture that incorporates several key innovations into the existing framework of 3D-aware GANs, such as StyleGAN2 and EG3D. The authors present a two-stage self-adaptive image alignment strategy, crucial for handling the wide variety of poses inherent in in-the-wild datasets. This alignment process is essential to reduce the noise and discrepancies typically encountered with large view angles during training.

A significant enhancement is the introduction of a tri-grid neural volume representation. This innovation directly addresses the limitations of the traditional tri-plane formulation, which suffers from feature entanglement issues, especially when attempting to synthesize the back of the head. The tri-grid representation enables more precise and efficient encoding of volumetric features by providing a three-dimensional depth mapping that disentangles the front from the back neural features, thus resolving the "mirrored face" problem common in 3D head synthesis.

Furthermore, the paper details the development of a foreground-aware tri-discriminator. This component intelligently separates foreground from background during adversarial learning, leveraging 2D image segmentation techniques to enhance the 3D synthesis process. This method allows for realistic compositionality of synthesized heads against diverse and changing backgrounds, significantly improving the SNR of geometry details and the aesthetic quality of generated images.

Empirical and Theoretical Outcomes

PanoHead's performance is comprehensively assessed through a combination of qualitative and quantitative analyses. The results demonstrate significant improvements over existing state-of-the-art methods such as GRAF, GIRAFFEHD, StyleSDF, and even its predecessor, EG3D. Metrics including FID, ID scores, and segmentation MSE showcase PanoHead's capabilities to generate higher fidelity images with lower error rates and better identity preservation across angles. The versatility of PanoHead is further evidenced in its ability to consider a broader range of camera poses, including hard-to-capture back head features, which previous models often failed to model accurately due to the lack of explicit view distribution considerations.

The authors also illustrate practical applications of PanoHead in the reconstruction of 3D avatars from single-view images, showing its potential impact on industries such as gaming, telepresence, and digital media. The capability to render highly detailed and dynamically accurate 3D avatars from minimal input significantly enhances current methods used in these domains.

Future Implications

The methodologies and findings in the PanoHead paper set the stage for several future research directions. There is potential to further enhance the tri-grid representation's scalability and efficiency, ensuring wider adoption across various 3D synthesis tasks. Future work might also explore the integration of advanced neural rendering techniques more deeply rooted in photometric consistency, potentially overcoming current limitations in fine-scale textural synthesis, such as hair and skin details.

PanoHead's approach of using a tri-grid representation along with better data alignment mechanisms can inspire enhanced 3D generative techniques across different objects and environments beyond just human heads. Additionally, the addressing of ethical considerations, such as misuse in the form of deepfakes, will remain a crucial area of consideration as these models are developed for larger-scale deployment.

Ultimately, PanoHead marks a significant methodological step towards more realistic and flexible 3D synthesis, advancing both theoretical understanding and practical capabilities within the field of computer-generated imagery.