Summary of Fabricdiffusion: High-fidelity Texture Transfer For 3d Garments Generation From In-the-wild Clothing Images, by Cheng Zhang and Yuanhao Wang and Francisco Vicente Carrasco and Chenglei Wu and Jinlong Yang and Thabo Beeler and Fernando De La Torre
FabricDiffusion: High-Fidelity Texture Transfer for 3D Garments Generation from In-The-Wild Clothing Images
by Cheng Zhang, Yuanhao Wang, Francisco Vicente Carrasco, Chenglei Wu, Jinlong Yang, Thabo Beeler, Fernando De la Torre
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 FabricDiffusion is a novel method for transferring fabric textures from 2D clothing images to 3D garments of arbitrary shapes. Unlike existing approaches that struggle with capturing texture details due to occlusions, distortions, or poses, FabricDiffusion extracts distortion-free, tileable texture materials and maps them onto the UV space of the garment. By training a denoising diffusion model on a large-scale synthetic dataset, FabricDiffusion rectifies distortions in the input texture image, enabling realistic relighting of the garment under various lighting conditions. Experimental results demonstrate that FabricDiffusion outperforms state-of-the-art methods on both synthetic and real-world data, generalizing to unseen textures and garment shapes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to transfer the texture from a single clothing image onto any 3D garment shape you want! This is what the new method called FabricDiffusion does. It takes an image of a piece of clothing and uses it to create a realistic texture for another garment with a different shape. This is important because current methods often struggle to capture the right texture details, especially when there are things in the way or the garment is bent or twisted. FabricDiffusion solves this problem by creating a special kind of texture map that can be used to make the new garment look realistic. It’s like taking a picture of a piece of clothing and then using it as a template to create a new one with a different shape! |
Keywords
» Artificial intelligence » Diffusion model
