ClothFormer: Taming Video Virtual Try-on in All Module(CVPR 2022 Oral)

In order to reduce the project size, we put all videos of paper's figures on google drive.

Introduction

Framework of ClothFormer. (a) First, we obtain clothing-agnostic person image sequences. (b) We predict t warped clothes by TPS-based warp method to infer an anti-occlusion target clothes, then appearance-flow-based warp method is adopted to get an appearance flow. (c) Appearance-flow tracking module based on ridge regression and optical flow correction is designed to get warped clothing sequence with spatio-temporally consistent. (d) Finally, MPDT generator synthesizes the final output video sequence based on the outputs from the previous stages.

Results

Results on iQIYI-VVT

Results on VVT

Citation

@inproceedings{jiang2022clothformer,
    title={ClothFormer: Taming Video Virtual Try-on in All Module},
    author={Jianbin Jiang and Tan Wang and He Yan and Junhui Liu},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2022}
}