## AnyMaker: Zero-shot General Object Customization via Decoupled Dual-Level ID Injection [[Paper](https://arxiv.org/abs/2406.11643v2)]   [[Project Page](https://lingjiekong-fdu.github.io/)]  

### **Core Properties:** 1. **General field:** Applicable to general objects, including but not limited to: human face, clothes, tools, animal, ... 2. **Fast generation:** Achieve object customization within seconds, without any further fine-tuning. 3. **User friendly:** Just one single reference image and a text prompt can meet the customization requirement. 4. **Outstanding results:** Ensure high ID fidelity, flexible text editability and high quality. 5. **Various applications:** Diverse applications such as general object customization, virtual try-on, ID-mixing, ... --- ### **Method Framework:** AnyMaker consists of three crucial ID processing modules: General ID Extraction Module, Dual-Level ID Injection Module, ID-Aware Decoupling Module.

--- ### **Comparisons with Previous Works:** The AnyMaker exhibits outstanding capabilities of high-quality customization for general objects, and even beat task-specialized methods in the specific domains, such as human customization and virtual try-on, in terms of ID fidelity and text editability.

--- ### **General ID Dataset:** To promot the research of the general object customization, we construct the first large-scale general ID dataset, named as Multi-Category ID-Consistent (MC-IDC) dataset. Our dataset consists of approximately 315,000 samples in total with more than 10,000 categories, covering various types such as human faces, animals, clothes, human-made tools, etc. Each sample consists of a reference image, a segmentation mask of the object of interest in the reference image, a target image, and a text caption of the target image. The reference image with its segmentation mask provides ID information, the text caption of the target image offers semantic-level guidance for generation, and the target image serves as the ground truth.

@article{kong2024anymaker,
  title={AnyMaker: Zero-shot General Object Customization via Decoupled Dual-Level ID Injection},
  author={Kong, Lingjie and Wu, Kai and Hu, Xiaobin and Han, Wenhui and Peng, Jinlong and Xu, Chengming and Luo, Donghao and Zhang, Jiangning and Wang, Chengjie and Fu, Yanwei},
  journal={arXiv preprint arXiv:2406.11643},
  year={2024}
}