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
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).
68a29748a4caabb4320e640d
Seungmi Lee
68a29748a4caabb4320e640e
Kwan Yun
68a29748a4caabb4320e640f
Junyong Noh
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
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).
StyleMM constructs stylized 3DMMs from text descriptions using a diffusion model for image-to-image translation while preserving facial attributes.
3D Morphable Model 3DMM mesh deformation network texture generator text-guided image-to-image translation diffusion model stylization method facial attributes identity-level facial diversity stylization capability