CVPR 2023 – GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning

In this episode we discuss GP-VTON: Towards General Purpose Virtual Try-on via Collaborative Local-Flow Global-Parsing Learning
by Zhenyu Xie, Zaiyu Huang, Xin Dong, Fuwei Zhao, Haoye Dong, Xijin Zhang, Feida Zhu, Xiaodan Liang. The paper proposes a General-Purpose Virtual Try-ON framework, named GP-VTON, for transferring a garment onto a specific person. The proposed framework addresses the limitations of existing methods which fail to preserve semantic information of the garment parts, result in texture distortion and limit the scalability of the system. It introduces a Local-Flow Global-Parsing (LFGP) warping module and a Dynamic Gradient Truncation (DGT) training strategy, resulting in better warping of different garment parts and avoiding texture squeezing. The proposed framework outperforms existing state-of-the-art methods on two high-resolution benchmarks.