StyleMM: Stylized 3D Morphable Face Model via Text‐Driven Aligned Image Translation

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...

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Bibliographic Details
Published in:Computer graphics forum Vol. 44; no. 7
Main Authors: Lee, Seungmi, Yun, Kwan, Noh, Junyong
Format: Journal Article
Language:English
Published: Oxford Blackwell Publishing Ltd 01.10.2025
Subjects:
CCS Concepts
Computer graphics
Computer vision representations
Computing methodologies → Computer graphics
Parameters
Texture
ISSN:0167-7055, 1467-8659
Online Access:Get full text
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Summary: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. Categories and Subject Descriptors (according to ACM CCS): I.3.6 [Computer Graphics]: Methodology and Techniques—
Bibliography:Contributed equally to this work
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.70234