DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability

• URL: http://arxiv.org/abs/2503.06505v3
• Date: Mon, 21 Jul 2025 07:11:27 GMT
• Title: DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability
• Authors: Xirui Hu, Jiahao Wang, Hao Chen, Weizhan Zhang, Benqi Wang, Yikun Li, Haishun Nan,
• Abstract summary: We present DynamicID, a tuning-free framework that inherently facilitates both single-ID and multi-ID personalized generation.<n>Our key innovations include: 1) Semantic-Activated Attention (SAA), which employs query-level activation gating to minimize disruption to the base model when injecting ID features and achieve multi-ID personalization without requiring multi-ID samples during training; 2) Identity-Motion Reconfigurator (IMR), which applies feature-space manipulation to effectively disentangle facial motion and identity features, supporting flexible facial editing; and 3) a task-decoupled training paradigm that reduces data dependency, together with VariFace-10k,
• Score: 12.692129257068085
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in text-to-image generation have driven interest in generating personalized human images that depict specific identities from reference images. Although existing methods achieve high-fidelity identity preservation, they are generally limited to single-ID scenarios and offer insufficient facial editability. We present DynamicID, a tuning-free framework that inherently facilitates both single-ID and multi-ID personalized generation with high fidelity and flexible facial editability. Our key innovations include: 1) Semantic-Activated Attention (SAA), which employs query-level activation gating to minimize disruption to the base model when injecting ID features and achieve multi-ID personalization without requiring multi-ID samples during training. 2) Identity-Motion Reconfigurator (IMR), which applies feature-space manipulation to effectively disentangle and reconfigure facial motion and identity features, supporting flexible facial editing. 3) a task-decoupled training paradigm that reduces data dependency, together with VariFace-10k, a curated dataset of 10k unique individuals, each represented by 35 distinct facial images. Experimental results demonstrate that DynamicID outperforms state-of-the-art methods in identity fidelity, facial editability, and multi-ID personalization capability. Our code will be released at https://github.com/ByteCat-bot/DynamicID.

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