AvatarArtist：开放域4D虚拟化身生成
AvatarArtist: Open-Domain 4D Avatarization
AI总结：
本文提出AvatarArtist，利用GAN与扩散模型协同训练，从任意风格肖像图生成高质量4D化身，构建多域数据集，实现开放域鲁棒生成。
摘要：
本工作聚焦于开放域4D虚拟化身生成，旨在从任意风格的肖像图像创建4D化身。我们选择参数化三平面作为中间4D表示，并提出一种实用的训练范式，该范式同时利用了生成对抗网络（GAN）和扩散模型的优势。我们的设计源于以下观察：4D GAN擅长在无监督情况下桥接图像与三平面，但通常在处理多样数据分布时面临挑战。一个鲁棒的2D扩散先验作为解决方案出现，帮助GAN将其专长迁移到不同领域。这些专家之间的协同作用使得构建多域图像-三平面数据集成为可能，从而推动通用4D化身创建器的发展。大量实验表明，我们的模型AvatarArtist能够生成高质量的4D化身，并对各种源图像域具有很强的鲁棒性。代码、数据和模型将公开提供，以促进未来的研究。
英文摘要：
This work focuses on open-domain 4D avatarization, with the purpose of creating a 4D avatar from a portrait image in an arbitrary style. We select parametric triplanes as the intermediate 4D representation and propose a practical training paradigm that takes advantage of both generative adversarial networks (GANs) and diffusion models. Our design stems from the observation that 4D GANs excel at bridging images and triplanes without supervision yet usually face challenges in handling diverse data distributions. A robust 2D diffusion prior emerges as the solution, assisting the GAN in transferring its expertise across various domains. The synergy between these experts permits the construction of a multi-domain image-triplane dataset, which drives the development of a general 4D avatar creator. Extensive experiments suggest that our model, AvatarArtist, is capable of producing high-quality 4D avatars with strong robustness to various source image domains. The code, the data, and the models will be made publicly available to facilitate future studies.
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。
