Summary of Ragdiffusion: Faithful Cloth Generation Via External Knowledge Assimilation, by Xianfeng Tan et al.
RAGDiffusion: Faithful Cloth Generation via External Knowledge Assimilation
by Xianfeng Tan, Yuhan Li, Wenxiang Shang, Yubo Wu, Jian Wang, Xuanhong Chen, Yi Zhang, Ran Lin, Bingbing Ni
First submitted to arxiv on: 29 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework called RAGDiffusion for generating standard clothing asset images. The framework aims to address the challenges of extracting clothing information from diverse real-world contexts, including highly standardized sampling distributions and precise structural requirements. Existing models have limited spatial perception and often exhibit structural hallucinations in this high-specification generative task. To overcome these limitations, RAGDiffusion employs a Retrieval-Augmented Generation (RAG) approach that assimilates external knowledge from Large Language Models (LLM) and databases. The framework consists of two core processes: retrieval-based structure aggregation and omni-level faithful garment generation. The former uses contrastive learning and Structure Locally Linear Embedding (SLLE) to derive global structure and spatial landmarks, providing soft and hard guidance to counteract structural ambiguities. The latter introduces a three-level alignment that ensures fidelity in structural, pattern, and decoding components within the diffusing. Experimental results on real-world datasets demonstrate significant performance improvements, representing a pioneering effort in high-specification faithful generation with RAG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating pictures of clothes for computer graphics. It’s hard because we need to make sure the clothes look right and don’t get mixed up with other things. Current methods are not good at this because they can’t see the whole picture. To fix this, scientists came up with a new way called RAGDiffusion that uses big computers to help make the pictures. This method has two parts: one finds the important features of the clothes and another makes sure the picture is accurate. The results are amazing! |
Keywords
* Artificial intelligence * Alignment * Embedding * Rag * Retrieval augmented generation
