CustAny: Customizing Anything from A Single Example
Lingjie Kong, Kai Wu, Xiaobin Hu, Wenhui Han, Jinlong Peng, Chengming, Xu, Donghao Luo, Mengtian Li, Jiangning Zhang, Chengjie Wang, Yanwei Fu

TL;DR
CustAny introduces a zero-shot framework for object customization from a single reference, leveraging a large-scale dataset and novel ID processing to enhance fidelity across diverse objects and domains.
Contribution
The paper presents a large-scale general object dataset and a new zero-shot customization framework with innovative ID extraction and injection modules.
Findings
Outperforms existing methods in general object customization
Effective in specialized domains like human and virtual try-on
Supports flexible text editing for customized objects
Abstract
Recent advances in diffusion-based text-to-image models have simplified creating high-fidelity images, but preserving the identity (ID) of specific elements, like a personal dog, is still challenging. Object customization, using reference images and textual descriptions, is key to addressing this issue. Current object customization methods are either object-specific, requiring extensive fine-tuning, or object-agnostic, offering zero-shot customization but limited to specialized domains. The primary issue of promoting zero-shot object customization from specific domains to the general domain is to establish a large-scale general ID dataset for model pre-training, which is time-consuming and labor-intensive. In this paper, we propose a novel pipeline to construct a large dataset of general objects and build the Multi-Category ID-Consistent (MC-IDC) dataset, featuring 315k text-image…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Web Data Mining and Analysis · Advanced Malware Detection Techniques
MethodsDiffusion
