ArtNeRF: A Generative Neural Field for Synthesizing 3D-Aware Cartoon Faces with Arbitrary Styles

Core Concepts

ArtNeRF is a novel 3D-aware GAN framework that can generate high-quality, multi-view cartoon faces with arbitrary styles from given reference images.

Abstract

The paper proposes ArtNeRF, a 3D-aware GAN framework for generating multi-view cartoon faces with arbitrary styles. The key components include:

A self-supervised style encoder that extracts robust low-dimensional style embeddings while preventing the leakage of pose information from the reference images.

A conditional generative radiance field with dense skip connections and a neural rendering module, which enhances the generator's ability to synthesize high-quality 3D-aware faces with efficient real-time rendering.

A self-adaptive style blending module that dynamically adjusts the blending ratio of style control vectors to ensure a smooth training process during cross-domain adaptation.

A triple-branch discriminator network that supervises the generator to produce 3D-aware stylized faces adhering to the distributions of both the source and target domains, while also improving style consistency between the generated faces and the reference images.

Extensive experiments demonstrate that ArtNeRF can generate high-quality 3D-aware cartoon faces with arbitrary styles, outperforming existing 2D and 3D-aware stylization methods in terms of visual quality, style consistency, and multi-view consistency.

Stats

The authors use the CelebA dataset containing around 200k faces as the source domain, and the AAHQ dataset comprising around 24k high-quality stylized faces as the style domain.

Quotes

"Recent advances in generative visual models and neural radiance fields have greatly boosted 3D-aware image synthesis and stylization tasks. However, previous NeRF-based work is limited to single scene stylization, training a model to generate 3D-aware cartoon faces with arbitrary styles remains unsolved."
"We propose ArtNeRF, a novel face stylization framework derived from 3D-aware GAN to tackle this problem."

Key Insights Distilled From

ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis

by Zichen Tang,... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.13711.pdf

Deeper Inquiries

How could ArtNeRF be extended to handle more diverse facial expressions and head poses beyond the frontal view

ArtNeRF can be extended to handle more diverse facial expressions and head poses beyond the frontal view by incorporating additional training data that includes a wider range of facial expressions and head poses. This expanded dataset would allow the model to learn and generate faces with varying expressions and poses. Additionally, the model architecture can be modified to include pose estimation modules that can detect and adapt to different head poses during the synthesis process. By integrating these components, ArtNeRF can effectively generate 3D-aware stylized faces with diverse expressions and head poses.

What are the potential limitations of the current neural rendering module, and how could it be further improved to achieve even higher visual quality and rendering speed

The current neural rendering module in ArtNeRF may have limitations in handling extremely high-resolution images and complex scenes due to computational constraints. To further improve visual quality and rendering speed, the neural rendering module can be enhanced by implementing more efficient rendering algorithms, such as sparse convolutional networks or hierarchical neural rendering techniques. Additionally, optimizing the neural rendering process by leveraging hardware acceleration, parallel processing, and advanced rendering algorithms can help achieve higher visual quality and faster rendering speeds. By continuously refining and optimizing the neural rendering module, ArtNeRF can enhance its capabilities in generating high-quality 3D-aware stylized images.

Could the proposed techniques in ArtNeRF be applied to other 3D-aware generative tasks beyond face synthesis, such as full-body avatar generation or 3D scene stylization

The techniques proposed in ArtNeRF can be applied to other 3D-aware generative tasks beyond face synthesis, such as full-body avatar generation or 3D scene stylization. By adapting the framework to incorporate different input modalities, such as body poses and environmental features, the model can be trained to generate diverse full-body avatars or stylized 3D scenes. Additionally, the style blending and neural rendering modules can be modified to accommodate the unique characteristics of full-body avatars or complex 3D scenes, enabling the model to generate high-quality and stylized outputs in various 3D-aware generative tasks. Through these adaptations, ArtNeRF can be extended to address a wide range of 3D-aware generative applications beyond face synthesis.