I have shared many virtual try-on projects before, many of which are from Alibaba.
Today, let's take a look at IMAGDressing, a virtual try-on project from Tencent and Huawei, which was released last week. The code and model are available on GitHub: https://github.com/muzishen/IMAGDressing
Scenarios
The differences in tasks based on conditions and applicable scenarios:
focuses on how users can try on clothes in a virtual environment, achieving realistic try-on effects through localized clothing refinement to enhance the online shopping experience for consumers. emphasizes generating freely editable human images with fixed clothing and optional conditions (such as faces, poses, scenes), suitable for merchants' various needs to display clothing.
Methodology
IMAGDressing-v1 Framework Diagram
This framework mainly consists of a trainable clothing UNet and a frozen denoising UNet. The former extracts fine-grained clothing features, while the latter balances these features with text prompts. IMAGDressing-v1 is compatible with other community modules such as ControlNet and IP-Adapter.
Comparison
: Quantitative comparison of IMAGDressing-v1 with several state-of-the-art methods. : Qualitative comparison of IMAGDressing-v1 with other state-of-the-art methods (including BLIP-Diffusion, Versatile Diffusion, IP-Adapter, and MagicClothing) under non-specific and specific conditions.