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-# DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability
-
-<div align="center">
-    
-### [ICCV 2025]
-
-[Xirui Hu](https://openreview.net/profile?id=~Xirui_Hu1),
-[Jiahao Wang](https://openreview.net/profile?id=~Jiahao_Wang14),
-[Hao Chen](https://openreview.net/profile?id=~Hao_chen100),
-[Weizhan Zhang](https://openreview.net/profile?id=~Weizhan_Zhang1),
-[Benqi Wang](https://openreview.net/profile?id=~Benqi_Wang2),
-[Yikun Li](https://openreview.net/profile?id=~Yikun_Li1),
-[Haishun Nan](https://openreview.net/profile?id=~Haishun_Nan1),
-
-[![arXiv](https://img.shields.io/badge/arXiv-2503.06505-b31b1b.svg)](https://arxiv.org/abs/2503.06505)
-[![GitHub](https://img.shields.io/badge/GitHub-MTVCrafter-blue?logo=github)](https://github.com/ByteCat-bot/DynamicID)
-</div>
-
----
-This is the official implementation of DynamicID, a framework that generates visually harmonious image featuring **multiple individuals**. Each person in the image can be specified through user-provided reference images, and most notably, our method enables **independent control of each individual's facial expression** via text prompts. Hope you have fun with this demo!
-
----
-
-## 🔍 Abstract
-
-Recent advancements in text-to-image generation have spurred interest in personalized human image generation. Although existing methods achieve high-fidelity identity preservation, they often struggle with **limited multi-ID usability** and **inadequate 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: 
-
-- Semantic-Activated Attention (SAA), which employs query-level activation gating to minimize disruption to the original model when injecting ID features and achieve multi-ID personalization without requiring multi-ID samples during training. 
-
-- Identity-Motion Reconfigurator (IMR), which applies feature-space manipulation to effectively disentangle and reconfigure facial motion and identity features, supporting flexible facial editing.
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-- A task-decoupled training paradigm that reduces data dependency
-
-- A curated VariFace-10k facial dataset, comprising 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.
-
-## 💡 Method
-
-<div align="center">
-    <img src="assets/pipeline.jpg", width="1000">
-</div>
-
-The proposed framework is architected around two core components: SAA and IMR. (a) In the anchoring stage, we jointly optimize the SAA and a face encoder to establish robust single-ID and multi-ID personalized generation capabilities. (b) Subsequently in the reconfiguration stage, we freeze these optimized components and leverage them to train the IMR for flexible and fine-grained facial editing.
-
-## 🚀 Checkpoint
-
-1. Download the pretrained Stable Diffusion v1.5 checkpoint from [Stable Diffusion v1.5 on Hugging Face](https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5).
-
-2. Download our SAA-related and IMR-related checkpoints from  [DynamicID Checkpoints on Hugging Face](https://huggingface.co/meteorite2023/DynamicID).
-
-
-## 🌈 Gallery
-
-<div align="center">
-    <img src="assets/teaser.jpg", width="900">
-    <br><br><br>
-    <img src="assets/single.jpg", width="900">
-    <br><br><br>
-    <img src="assets/multi.jpg", width="900">
-</div>
-
-## 📌 ToDo List
-
-- [x] Release technical report
-- [x] Release **training and inference code**
-- [x] Release **Dynamic-sd** (based on *stable diffusion v1.5*)  
-- [ ] Release **Dynamic-flux** (based on *Flux-dev*)
-- [ ] Release a Hugging Face Demo Space
-
-## 📖 Citation
-If you are inspired by our work, please cite our paper.
-```bibtex
-@inproceedings{dynamicid,
-      title={DynamicID: Zero-Shot Multi-ID Image Personalization with Flexible Facial Editability},
-      author={Xirui Hu,
-              Jiahao Wang,
-              Hao Chen,
-              Weizhan Zhang,
-              Benqi Wang,
-              Yikun Li,
-              Haishun Nan
-              },
-      booktitle={International Conference on Computer Vision},
-      year={2025}
-    }
-    
-```