Stable 3D Full-head Synthesis with Spherical Tri-plane Representation

핵심 개념

SphereHead, a novel framework for high-quality, view-consistent full head image synthesis, leverages a unique spherical tri-plane representation and a view-image consistency loss to significantly mitigate prevalent semantic mismatch artifacts.

초록

The paper proposes SphereHead, a novel 3D-aware GAN framework for high-quality, view-consistent full head image synthesis. The key innovations are:

Spherical Tri-plane Representation:

Represents the 3D head using a dual spherical tri-plane feature, which effectively separates features from different facial regions and eliminates mirroring artifacts.
The spherical representation aligns with the geometric characteristics of the human head, providing an efficient and artifact-free feature encoding.

View-Image Consistency Loss:

Introduces a view-image consistency loss to compel the discriminator to focus on the alignment between images and their viewpoints, minimizing artifacts arising from imbalanced supervision.

Parsing Branch:

Incorporates a parsing branch in the renderer to enhance the generator's semantic understanding of facial regions, leading to improved visual quality and artifact reduction.
The paper also presents a large-scale dataset of 200k non-frontal head images, which is carefully curated and will be publicly released. Extensive experiments demonstrate that SphereHead outperforms state-of-the-art 3D-aware GANs in terms of image quality, diversity, and artifact reduction.

통계

"The synthesis of human head portrait images has long been of great concern within the realms of computer vision and graphics, with extensive applications spanning video conferencing, digital avatars, games, movies, and more."
"PanoHead [1], the first 3D-aware GAN for full head synthesis, addresses this need to an extent. It enables the training with monocular back-view images using an adaptive camera strategy and extends the tri-plane representation into a tri-grid version for back-view synthesis."

인용구

"While recent advances in 3D-aware Generative Adversarial Networks (GANs) have aided the development of near-frontal view human face synthesis, the challenge of comprehensively synthesizing a full 3D head viewable from all angles still persists."
"From the network architecture perspective, representations based on the Cartesian coordinate system, such as tri-plane and tri-grid, naturally introduce feature entanglement for symmetrical areas relative to the predefined planes."
"From the data supervision aspect, in 3D-aware generation, it's crucial to ensure the correspondence between the generated images and their viewing perspectives."

핵심 통찰 요약

SphereHead

by Heyuan Li,Ce... 게시일 arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05680.pdf

더 깊은 질문

How can the proposed spherical tri-plane representation be extended to other 3D-aware synthesis tasks beyond human heads?

The proposed spherical tri-plane representation can be extended to other 3D-aware synthesis tasks by adapting the concept of separating features based on geometric characteristics to fit the specific object being synthesized. For instance, in tasks involving objects with asymmetrical features or complex structures, a dual spherical tri-plane representation could be designed to effectively capture and differentiate features from different regions. By aligning the representation with the geometric properties of the object, similar to how it fits the human head's characteristics, the model can avoid feature entanglement and reduce artifacts in the synthesis process. This approach can be applied to various objects with distinct shapes and features, enabling more accurate and realistic 3D synthesis across different domains.

What are the potential limitations of the view-image consistency loss, and how can it be further improved to handle more complex 3D synthesis scenarios?

One potential limitation of the view-image consistency loss is its reliance on the quality of the camera parameters and the images to ensure alignment. In more complex 3D synthesis scenarios where there are multiple objects, intricate structures, or varying lighting conditions, the consistency between images and viewpoints may be challenging to maintain. To address this limitation and improve the effectiveness of the view-image consistency loss, additional constraints or regularization techniques can be incorporated. For example, introducing constraints based on object segmentation or depth estimation can help enhance the model's understanding of the scene geometry and improve viewpoint consistency. Moreover, leveraging advanced techniques such as attention mechanisms or multi-view supervision can further enhance the model's ability to handle complex 3D synthesis scenarios by capturing more detailed spatial relationships and context.

Given the diverse hairstyles in the collected dataset, how can the model's capacity be enhanced to better handle complex hair patterns and structures in full-head synthesis?

To better handle complex hair patterns and structures in full-head synthesis, the model's capacity can be enhanced through several approaches:

Data Augmentation: Augmenting the dataset with a wider variety of hairstyles, including intricate patterns and diverse textures, can help the model learn to generate more realistic and detailed hair structures.

Advanced Architectures: Utilizing advanced neural network architectures, such as transformer-based models or hierarchical structures, can enable the model to capture intricate details and variations in hair patterns more effectively.

Fine-tuning and Transfer Learning: Fine-tuning the model on specific hair-related tasks or leveraging pre-trained models designed for hair synthesis can improve the model's ability to handle complex hair structures.

Incorporating Hair-specific Loss Functions: Introducing specialized loss functions that focus on hair attributes, such as texture, volume, and styling, can guide the model to generate more accurate and realistic hair patterns.

By combining these strategies and tailoring the training process to focus on the nuances of hair synthesis, the model's capacity can be enhanced to better handle the diverse and complex hairstyles present in the dataset.