Abstract
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 [this http URL](this http URL).
Abstract (translated)
我们介绍了一种新颖的框架StyleMM，它可以基于用户定义的文字描述构建具有特定目标风格的个性化3D可变模型（3DMM）。此方法在预训练的网格变形网络和用于原始3DMM真实感人脸的纹理生成器基础上进行微调，并使用文本引导的图像到图像（i2i）转换的扩散模型来生成风格化的面部图像。这些风格化后的图像作为渲染网格的风格化目标，以构建基于用户定义文字描述的个性化3D可变模型。 为了防止在i2i翻译过程中身份、面部对齐或表情的变化不受控制，我们引入了一种可以显式保留源图关键属性的风格化方法。通过在图像风格化过程中保持这些重要属性，所提出的方法确保了基于图像训练的一致性3D风格转换跨越整个3DMM参数空间。 一旦完成训练，StyleMM能够进行前馈生成具有明确形状、表情和纹理参数控制的风格化面部网格，并且这些网格具备一致的顶点连接性和可动画性。定量和定性的评估表明，在身份层面的面部多样性及风格化能力方面，我们的方法超过了当前最先进的技术。 相关代码和视频可在[此链接](this http URL)获取。
URL
https://arxiv.org/abs/2508.11203
