面向仅使用图像的高保真文本引导三维人脸生成与操控
Towards High-Fidelity Text-Guided 3D Face Generation and Manipulation Using only Images
AI总结：
本文提出TG-3DFace方法，利用文本引导生成高保真三维人脸，通过全局对比学习与细粒度对齐提升语义一致性，并引入方向性分类器引导增强创造性，在MVIC上较Latent3D提升9%。
摘要：
从文本描述生成三维人脸具有众多应用，例如游戏、电影和机器人技术。近年来的进展已证明了无条件三维人脸生成和文本到三维形状生成的成功。然而，由于文本-三维人脸数据对有限，文本驱动的三维人脸生成仍是一个开放性问题。在本文中，我们提出了一种文本引导的三维人脸生成方法，称为TG-3DFace，用于利用文本引导生成逼真的三维人脸。具体而言，我们采用了一个无条件三维人脸生成框架，并为其配备文本条件，仅使用文本-二维人脸数据即可学习文本引导的三维人脸生成。在此基础上，我们提出了两种文本到人脸跨模态对齐技术，包括全局对比学习和细粒度对齐模块，以促进生成的三维人脸与输入文本之间的高语义一致性。此外，我们在推理过程中引入了方向性分类器引导，以鼓励域外生成的创造性。与现有方法相比，TG-3DFace生成了更逼真且更具美感的三维人脸，在Latent3D基础上将多视角一致性（MVIC）提升了9%。由TG-3DFace生成的渲染人脸图像在FID和CLIP分数上优于文本到二维人脸/图像生成模型，展示了我们在生成逼真且语义一致的纹理方面的优越性。
英文摘要：
Generating 3D faces from textual descriptions has a multitude of applications, such as gaming, movie, and robotics. Recent progresses have demonstrated the success of unconditional 3D face generation and text-to-3D shape generation. However, due to the limited text-3D face data pairs, text-driven 3D face generation remains an open problem. In this paper, we propose a text-guided 3D faces generation method, refer as TG-3DFace, for generating realistic 3D faces using text guidance. Specifically, we adopt an unconditional 3D face generation framework and equip it with text conditions, which learns the text-guided 3D face generation with only text-2D face data. On top of that, we propose two text-to-face cross-modal alignment techniques, including the global contrastive learning and the fine-grained alignment module, to facilitate high semantic consistency between generated 3D faces and input texts. Besides, we present directional classifier guidance during the inference process, which encourages creativity for out-of-domain generations. Compared to the existing methods, TG-3DFace creates more realistic and aesthetically pleasing 3D faces, boosting 9% multi-view consistency (MVIC) over Latent3D. The rendered face images generated by TG-3DFace achieve higher FID and CLIP score than text-to-2D face/image generation models, demonstrating our superiority in generating realistic and semantic-consistent textures.
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。
