StyleMM：通过文本驱动的对齐图像翻译构建风格化三维可变形人脸模型
StyleMM: Stylized 3D Morphable Face Model via Text-Driven Aligned Image Translation
AI总结：
StyleMM提出通过文本引导的图像翻译风格化人脸图像来微调3DMM生成模型，在保留身份、对齐和表情属性的同时实现可控、可动画化的风格化三维人脸生成，并在多样性和风格化能力上优于现有方法。
摘要：
我们提出StyleMM，这是一个新颖的框架，能够根据用户定义的、指定目标风格的文本描述来构建风格化三维可变形模型（3DMM）。在预训练的网格变形网络和面向原始基于3DMM的真实感人脸的纹理生成器基础上，我们的方法利用通过扩散模型进行文本引导的图像到图像（i2i）翻译所生成的风格化人脸图像对这些模型进行微调，这些图像作为渲染网格的风格化目标。为了防止在i2i翻译过程中发生不希望的身份、人脸对齐或表情变化，我们引入了一种显式保留源图像人脸属性的风格化方法。通过在图像风格化过程中保持这些关键属性，所提出的方法确保在基于图像的训练中实现跨3DMM参数空间的一致三维风格迁移。训练完成后，StyleMM能够以前馈方式生成风格化人脸网格，并显式控制形状、表情和纹理参数，所生成的网格具有一致的顶点连接性和可动画化能力。定量和定性评估表明，我们的方法在身份级人脸多样性和风格化能力方面优于当前最先进的方法。代码和视频可在[kwanyun.github.io/stylemm_page](kwanyun.github.io/stylemm_page)获取。
英文摘要：
We introduce StyleMM, a novel framework that can construct a stylized 3D Morphable Model (3DMM) based on user-defined text descriptions specifying a target style. Building upon a pre-trained mesh deformation network and a texture generator for original 3DMM-based realistic human faces, our approach fine-tunes these models using stylized facial images generated via text-guided image-to-image (i2i) translation with a diffusion model, which serve as stylization targets for the rendered mesh. To prevent undesired changes in identity, facial alignment, or expressions during i2i translation, we introduce a stylization method that explicitly preserves the facial attributes of the source image. By maintaining these critical attributes during image stylization, the proposed approach ensures consistent 3D style transfer across the 3DMM parameter space through image-based training. Once trained, StyleMM enables feed-forward generation of stylized face meshes with explicit control over shape, expression, and texture parameters, producing meshes with consistent vertex connectivity and animatability. Quantitative and qualitative evaluations demonstrate that our approach outperforms state-of-the-art methods in terms of identity-level facial diversity and stylization capability. The code and videos are available at [kwanyun.github.io/stylemm_page](kwanyun.github.io/stylemm_page).
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。
