Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866912560265035776 |
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| author | Zou, Xuechao Zhang, Shun Fu, Xing Li, Yue Li, Kai Cao, Yushe Lang, Congyan Tao, Pin Xing, Junliang |
| author_facet | Zou, Xuechao Zhang, Shun Fu, Xing Li, Yue Li, Kai Cao, Yushe Lang, Congyan Tao, Pin Xing, Junliang |
| contents | Controllable face generation poses critical challenges in generative modeling due to the intricate balance required between semantic controllability and photorealism. While existing approaches struggle with disentangling semantic controls from generation pipelines, we revisit the architectural potential of Diffusion Transformers (DiTs) through the lens of expert specialization. This paper introduces Face-MoGLE, a novel framework featuring: (1) Semantic-decoupled latent modeling through mask-conditioned space factorization, enabling precise attribute manipulation; (2) A mixture of global and local experts that captures holistic structure and region-level semantics for fine-grained controllability; (3) A dynamic gating network producing time-dependent coefficients that evolve with diffusion steps and spatial locations. Face-MoGLE provides a powerful and flexible solution for high-quality, controllable face generation, with strong potential in generative modeling and security applications. Extensive experiments demonstrate its effectiveness in multimodal and monomodal face generation settings and its robust zero-shot generalization capability. Project page is available at https://github.com/XavierJiezou/Face-MoGLE. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_00428 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation Zou, Xuechao Zhang, Shun Fu, Xing Li, Yue Li, Kai Cao, Yushe Lang, Congyan Tao, Pin Xing, Junliang Computer Vision and Pattern Recognition Controllable face generation poses critical challenges in generative modeling due to the intricate balance required between semantic controllability and photorealism. While existing approaches struggle with disentangling semantic controls from generation pipelines, we revisit the architectural potential of Diffusion Transformers (DiTs) through the lens of expert specialization. This paper introduces Face-MoGLE, a novel framework featuring: (1) Semantic-decoupled latent modeling through mask-conditioned space factorization, enabling precise attribute manipulation; (2) A mixture of global and local experts that captures holistic structure and region-level semantics for fine-grained controllability; (3) A dynamic gating network producing time-dependent coefficients that evolve with diffusion steps and spatial locations. Face-MoGLE provides a powerful and flexible solution for high-quality, controllable face generation, with strong potential in generative modeling and security applications. Extensive experiments demonstrate its effectiveness in multimodal and monomodal face generation settings and its robust zero-shot generalization capability. Project page is available at https://github.com/XavierJiezou/Face-MoGLE. |
| title | Mixture of Global and Local Experts with Diffusion Transformer for Controllable Face Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.00428 |