ArtNeRF：一种用于3D感知卡通化人脸合成的风格化神经场
ArtNeRF: A Stylized Neural Field for 3D-Aware Cartoonized Face Synthesis
AI总结：
提出ArtNeRF框架，结合3D感知GAN、对比学习风格编码器与自适应风格混合模块，实现了高质量、任意风格的3D感知卡通人脸合成与实时渲染。
摘要：
生成式视觉模型和神经辐射场的最新进展极大地推动了3D感知图像合成与风格化任务。然而，以往的基于NeRF的工作仅限于单场景风格化，训练一个模型来生成具有任意风格的3D感知卡通人脸仍未解决。我们提出ArtNeRF，一个源自3D感知GAN的新颖人脸风格化框架来解决此问题。在此框架中，我们利用一个富有表现力的生成器来合成风格化人脸，并利用一个三分支判别器模块来提升生成人脸的视觉质量和风格一致性。具体而言，利用基于对比学习的风格编码器提取风格图像的鲁棒低维嵌入，赋予生成器各种风格的知识。为平滑跨域迁移学习的训练过程，我们提出一个自适应风格混合模块，它有助于注入风格信息并允许用户自由调节风格化程度。我们进一步引入一个神经渲染模块，以实现对更高分辨率图像的高效实时渲染。大量实验表明，ArtNeRF在生成具有任意风格的高质量3D感知卡通人脸方面具有通用性。
英文摘要：
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. In this framework, we utilize an expressive generator to synthesize stylized faces and a triple-branch discriminator module to improve the visual quality and style consistency of the generated faces. Specifically, a style encoder based on contrastive learning is leveraged to extract robust low-dimensional embeddings of style images, empowering the generator with the knowledge of various styles. To smooth the training process of cross-domain transfer learning, we propose an adaptive style blending module which helps inject style information and allows users to freely tune the level of stylization. We further introduce a neural rendering module to achieve efficient real-time rendering of images with higher resolutions. Extensive experiments demonstrate that ArtNeRF is versatile in generating high-quality 3D-aware cartoon faces with arbitrary styles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
