We introduce StyleMM, a novel framework that can construct a stylized 3D Morphable Model (3DMM) based on user\u2010defined text descriptions specifying a target style. Building upon a pre\u2010trained mesh deformation network and a texture generator for original 3DMM\u2010based realistic human faces, our approach fine\u2010tunes these models using stylized facial images generated via text\u2010guided image\u2010to\u2010image (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\u2010based training. Once trained, StyleMM enables feed\u2010forward 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\u2010of\u2010the\u2010art methods in terms of identity\u2010level facial diversity and stylization capability. The code and videos are available at kwanyun.github.io\/stylemm_page.<\/p>\n","protected":false},"excerpt":{"rendered":"
We introduce StyleMM, a novel framework that can construct a stylized 3D Morphable Model (3DMM) based on user\u2010defined text descriptions specifying a target style. 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