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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Fashion Matrix: Editing Your Photo by Just Talking">
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<title>Fashion Matrix: Editing Your Photo by Just Talking</title>
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<section class="hero">
<div class="hero-body">
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<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Fashion Matrix: Editing Your Photo by Just Talking</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="">Zheng Chong</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="">Xujie Zhang</a><sup>1</sup>,</span>
<span class="author-block">
<a href="">Fuwei Zhao</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Zhenyu Xie</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="">Xiaodan Liang</a><sup>*1,2</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>Shenzhen Campus of Sun Yat-Sen University,</span>
<span class="author-block"><sup>2</sup>Peng Cheng Laboratory</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- PDF Link. -->
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<a href="https://arxiv.org/pdf/2307.13240.pdf"
class="external-link button is-normal is-rounded is-dark">
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<span>Paper</span>
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class="external-link button is-normal is-rounded is-dark">
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<span>Video</span>
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<!-- Code Link. -->
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class="external-link button is-normal is-rounded is-dark">
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<span>Code</span>
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</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img src="./static/images/teaser.jpeg" alt="teaser">
<h2 class="subtitle has-text-centered">
Fashion Matrix demonstrates the capacity for engaging in multiple rounds of user dialogue, enabling proficient
and precise photo editing of individuals based on provided instructions.
</h2>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
The utilization of Large Language Models (LLMs) for the construction of AI systems has garnered significant
attention across diverse fields. The extension of LLMs to the domain of fashion holds substantial commercial
potential but also inherent challenges due to the intricate semantic interactions in fashion-related generation.
To address this issue, we developed a hierarchical AI system called Fashion Matrix dedicated to
editing photos by just talking. This system facilitates diverse prompt-driven tasks,
encompassing garment or accessory replacement, recoloring, addition, and removal.
</p>
<p>
Specifically, Fashion Matrix employs LLM as its foundational support and engages in iterative
interactions with users. It employs a range of Semantic Segmentation Models (e.g., Grounded-SAM,
MattingAnything, etc.) to delineate the specific editing masks based on user instructions. Subsequently,
Visual Foundation Models (e.g., Stable Diffusion, ControlNet, etc.) are leveraged to generate edited images
from text prompts and masks, thereby facilitating the automation of fashion editing processes.
</p>
<p>
Experiments demonstrate the outstanding ability of Fashion Matrix to explores the collaborative potential
of functionally diverse pre-trained models in the domain of fashion editing
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
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<h2 class="title is-3">Video</h2>
<div class="publication-video">
<iframe width="1080" height="1032" src="https://www.youtube.com/embed/1z-v0RSleMg"
title="Fashion Matrix: Editing Your Photo by Just Talking" frameborder="0"
allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
allowfullscreen></iframe>
</div>
</div>
</div>
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</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Architecture. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Architecture</h2>
<div class="content has-text-justified">
<p>
The system is composed of three modules: (1) Fashion Assistant , (2) Fashion Designer,
and (3) AutoMasker, which are at different levels, and all of them use LLM as the support of
intelligent text processing. Fashion Assistant engages in user interactions to collect
requirements, which are subsequently examined and transformed into instructions by Fashion Designer.
AutoMasker identifies the editing region based on the semantic context of the instructions.
Hierarchical design simplifies the logical processing flow and facilitates efficient information processing.
</p>
<img src="static/images/system.jpeg">
</div>
</div>
</div>
<!-- CoSegmentation. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">CoSegmentation</h2>
<div class="content has-text-justified">
<p>
AutoMasker combines human semantic segmentation with pose estimation to obtain a more fine-grained
semantic segmentation map - CoSegmentation.
</p>
<img src="static/images/cosegm.jpeg">
</div>
</div>
</div>
<!-- Functions. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Functions</h2>
<div class="content has-text-justified">
<p>
Fashion Matrix supports a wide range of editing tasks including region-specific
replacement, recoloring, addition, and removal of fashion concepts.
</p>
<img src="static/images/case.jpeg">
</div>
<br/>
<h3 class="title is-4">Replacement</h3>
<div class="content has-text-justified">
<p>
Results of the replacement task implemented by Fashion Matrix. The replaced target is integrated with the
source image seamlessly, adeptly accounting for authentic lighting conditions and occlusion challenges
</p>
<img src="static/images/replacement.jpeg">
</div>
<h3 class="title is-4">Recoloring</h3>
<div class="content has-text-justified">
<p>
Results of the recoloring task implemented by Fashion Matrix. Utilizing a dual set of mask and edge sketch,
the recoloring process is conjointly regulated, ensuring seamless integration of the generated output with
the unaltered regions, while preserving the shape of the original entity.
</p>
<img src="static/images/recolroing.jpeg">
</div>
<h3 class="title is-4">Removal</h3>
<div class="content has-text-justified">
<p>
Results of the replacement task implemented by Fashion Matrix. FashionMatrix's identification of entities
for removal is informed by the fine-grained CoSegmentation, coupled with the open domain segmentation
capabilities offered by Grounded-SAM.
</p>
<img src="static/images/removal.jpeg">
</div>
<h3 class="title is-4">Addition</h3>
<div class="content has-text-justified">
<p>
Results of the replacement task implemented by Fashion Matrix. Based on the positioning capabilities
of CoSegmentation and LLM, Fashion Matrix facilitates the incorporation of non-existent items into an image,
while ensuring coherence between the newly added entity and the original visual context.
</p>
<img src="static/images/addition.jpeg">
</div>
</div>
</div>
<!-- Concurrent Work. -->
<div class="columns is-centered">
<div class="column is-full-width">
<h2 class="title is-3">Related Links</h2>
<div class="content has-text-justified">
<p>
Our work is based on the following excellent works:
</p>
<p>
<a href="https://civitai.com/models/4201/realistic-vision-v20">Realistic Vision V4.0</a>
is a finely calibrated model derived from
<a href="https://github.com/Stability-AI/stablediffusion">Stable Diffusion</a> v1.5,
designed to enhance the realism of generated images, with a particular focus on human portraits.
</p>
<p>
<a href="https://github.com/lllyasviel/ControlNet-v1-1-nightly">ControlNet v1.1</a>
offers more comprehensive and user-friendly conditional control models,
enabling <a href="https://huggingface.co/docs/diffusers/v0.18.2/en/api/pipelines/controlnet#diffusers.StableDiffusionControlNetPipeline">
the concurrent utilization of multiple ControlNets</a>.
This significantly broadens the potential and applicability of text-to-image techniques.
</p>
<p>
<a href="https://github.com/salesforce/BLIP">BLIP</a> facilitates a rapid visual question-answering within our system</a>.
</p>
<p>
<a href="https://github.com/IDEA-Research/Grounded-Segment-Anything">Grounded-SAM</a>
create a very interesting demo by combining
<a href="https://github.com/IDEA-Research/GroundingDINO">Grounding DINO</a>
and <a href="https://github.com/facebookresearch/segment-anything">Segment Anything </a>
which aims to detect and segment anything with text inputs!
</p>
<p>
<a href="https://github.com/SHI-Labs/Matting-Anything">Matting Anything Model (MAM)</a> is an efficient and
versatile framework for estimating the alpha matte ofany instance in an image with flexible and interactive
visual or linguistic user prompt guidance.
</p>
<p>
<a href="https://github.com/facebookresearch/detectron2">Detectron2</a>
is a next generation library that provides state-of-the-art detection and segmentation algorithms.
The DensePose code we adopted is based on Detectron2.
</p>
<p>
<a href="https://github.com/Gaoyiminggithub/Graphonomy">Graphonomy</a> has the capacity for swift and
effortless analysis of diverse anatomical regions within the human body.
</p>
</div>
</div>
</div>
<!--/ Concurrent Work. -->
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@misc{chong2023fashion,
title={Fashion Matrix: Editing Photos by Just Talking},
author={Zheng Chong and Xujie Zhang and Fuwei Zhao and Zhenyu Xie and Xiaodan Liang},
year={2023},
eprint={2307.13240},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
</code></pre>
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