Summary of Fashionr2r: Texture-preserving Rendered-to-real Image Translation with Diffusion Models, by Rui Hu et al.
FashionR2R: Texture-preserving Rendered-to-Real Image Translation with Diffusion Models
by Rui Hu, Qian He, Gaofeng He, Jiedong Zhuang, Huang Chen, Huafeng Liu, Huamin Wang
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel framework for enhancing photorealism in rendered human images by leveraging generative models. The approach involves two stages: Domain Knowledge Injection (DKI) and Realistic Image Generation (RIG). DKI uses positive domain finetuning and negative domain embedding to inject knowledge into a Text-to-image (T2I) diffusion model, while RIG generates realistic images with Texture-preserving Attention Control (TAC) to preserve fine-grained clothing textures. The framework is evaluated on the SynFashion dataset, featuring high-quality digital clothing images with diverse textures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a new way to make computer-generated human images look more real. They used special algorithms and models that can learn from examples and create realistic pictures of people wearing different clothes. The goal was to take rendered images (like the ones you see in video games) and turn them into super-realistic images. The team created a special framework with two parts: one part helps the model understand what makes real images look so good, and another part generates the final image while preserving the details of the clothes. They tested their method on a large dataset of digital clothing images and showed that it works really well. |
Keywords
» Artificial intelligence » Attention » Diffusion model » Embedding » Image generation
