一种基于修复的服装与背景替换流程
An Inpainting-Infused Pipeline for Attire and Background Replacement
AI总结：
本文提出一种基于深度估计和修复的流程，利用稳定扩散与LCM自动替换服装和背景，无需手动掩码，实验证明其能生成视觉吸引力的内容。
摘要：
近年来，生成式人工智能（GenAI）的突破性进展引发了变革性的范式转变，显著影响了各个领域。在本工作中，我们专门探索了一种集成方法，利用GenAI和计算机视觉中的先进技术，强调图像操作。该方法通过几个阶段展开，包括深度估计、基于深度信息创建修复掩码、利用稳定扩散（Stable Diffusion）结合潜在一致性模型（LCMs）生成和替换背景，以及随后通过修复流程进行衣物替换和美学变化的应用。本研究中进行的实验强调了该方法的有效性，突显了其产生视觉吸引力内容的潜力。这些先进技术的融合允许用户输入个人照片，并根据特定提示修改服装和背景，无需手动输入修复掩码，从而有效地将主体置于广阔的创意想象景观中。
英文摘要：
In recent years, groundbreaking advancements in Generative Artificial Intelligence (GenAI) have triggered a transformative paradigm shift, significantly influencing various domains. In this work, we specifically explore an integrated approach, leveraging advanced techniques in GenAI and computer vision emphasizing image manipulation. The methodology unfolds through several stages, including depth estimation, the creation of inpaint masks based on depth information, the generation and replacement of backgrounds utilizing Stable Diffusion in conjunction with Latent Consistency Models (LCMs), and the subsequent replacement of clothes and application of aesthetic changes through an inpainting pipeline. Experiments conducted in this study underscore the methodology's efficacy, highlighting its potential to produce visually captivating content. The convergence of these advanced techniques allows users to input photographs of individuals and manipulate them to modify clothing and background based on specific prompts without manually input inpainting masks, effectively placing the subjects within the vast landscape of creative imagination.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
