Summary of Dpdedit: Detail-preserved Diffusion Models For Multimodal Fashion Image Editing, by Xiaolong Wang et al.
DPDEdit: Detail-Preserved Diffusion Models for Multimodal Fashion Image Editing
by Xiaolong Wang, Zhi-Qi Cheng, Jue Wang, Xiaojiang Peng
First submitted to arxiv on: 2 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 A new multimodal fashion image editing architecture, called Detail-Preserved Diffusion Models (DPDEdit), is introduced to accurately identify editing regions and preserve garment texture detail. DPDEdit integrates text prompts, region masks, human pose images, and garment texture images to guide diffusion models in generating fashion images. The architecture includes Grounded-SAM for predicting the editing region based on user descriptions, a texture injection and refinement mechanism using decoupled cross-attention layers and auxiliary U-Nets. Experiments show that DPDEdit outperforms state-of-the-art methods in terms of image fidelity and coherence with multimodal inputs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fashion image editing is important for designers to bring their ideas to life. Current techniques can struggle to accurately edit images, especially when it comes to preserving texture details. A new approach called Detail-Preserved Diffusion Models (DPDEdit) is designed to solve these problems. DPDEdit uses a combination of text prompts, image masks, and garment textures to guide the editing process. It also includes special techniques to predict where edits should be made and to preserve important details like texture. |
Keywords
» Artificial intelligence » Cross attention » Diffusion » Sam
