SwapAnything: Enabling Arbitrary Object Swapping in Personalized Visual Editing
Jing Gu, Nanxuan Zhao, Wei Xiong, Qing Liu, Zhifei Zhang, He Zhang,, Jianming Zhang, HyunJoon Jung, Yilin Wang, Xin Eric Wang

TL;DR
SwapAnything is a novel framework that enables precise, context-preserving, and personalized object swapping in images, significantly improving flexibility and fidelity over existing methods.
Contribution
The paper introduces targeted variable swapping and appearance adaptation techniques for improved, customizable object swapping in images, addressing limitations of prior approaches.
Findings
Outperforms baseline methods in personalized swapping tasks
Demonstrates precise control over arbitrary objects and parts
Effective across various swapping scenarios including cross-domain and text-based tasks
Abstract
Effective editing of personal content holds a pivotal role in enabling individuals to express their creativity, weaving captivating narratives within their visual stories, and elevate the overall quality and impact of their visual content. Therefore, in this work, we introduce SwapAnything, a novel framework that can swap any objects in an image with personalized concepts given by the reference, while keeping the context unchanged. Compared with existing methods for personalized subject swapping, SwapAnything has three unique advantages: (1) precise control of arbitrary objects and parts rather than the main subject, (2) more faithful preservation of context pixels, (3) better adaptation of the personalized concept to the image. First, we propose targeted variable swapping to apply region control over latent feature maps and swap masked variables for faithful context preservation and…
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Taxonomy
TopicsVideo Analysis and Summarization · Recommender Systems and Techniques · Multimodal Machine Learning Applications
