ACGPN
Adaptive Content Generating and Preserving Network
**ACGPN**, or **Adaptive Content Generating and Preserving Network**, is a [generative adversarial network](https://www.paperswithcode.com/method/category/generative-adversarial-network) for virtual try-on clothing applications. 

In Step I, the Semantic Generation Module (SGM) takes the target clothing image $\mathcal{T}\_{c}$, the pose map $\mathcal{M}\_{p}$, and the fused body part mask $\mathcal{M}^{F}$ as the input to predict the semantic layout and to output the synthesized body part mask $\mathcal{M}^{S}\_{\omega}$ and the target clothing mask $\mathcal{M}^{S\_{c}$.

In Step II, the Clothes Warping Module (CWM) warps the target clothing image to $\mathcal{T}^{R}\_{c}$ according to the predicted semantic layout, where a second-order difference constraint is introduced to stabilize the warping process. 

In Steps III and IV, the Content Fusion Module (CFM) first produces the composited body part mask $\mathcal{M}^{C}\_{\omega}$ using the original clothing mask $\mathcal{M}\_{c}$, the synthesized clothing mask $\mathcal{M}^{S}\_{c}$, the body part mask $\mathcal{M}\_{\omega}$, and the synthesized body part mask $\mathcal{M}\_{\omega}^{S}$, and then exploits a fusion network to generate the try-on images $\mathcal{I}^{S}$ by utilizing the information $\mathcal{T}^{R}\_{c}$, $\mathcal{M}^{S}\_{c}$, and the body part image $I\_{\omega}$ from previous steps.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content
https://paperswithcode.com/paper/towards-photo-realistic-virtual-try-on-by-1
Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content