This task targets at the video virtual try-on. Specifically, given a clothing image, a target person image, and a pose sequence, the participators are asked to design algorithms to transfer the desired clothing onto a person to produce a high-quality video sequence while preserving the identity information of the person, clothing texture details and the temporal coherence of the synthesized video.
We constructes a new video dataset VVT for the video virtual try-on task, which contains 791 videos of fashion model catwalk. We split the videos into a training set and a testing set with 661 videos and 130 videos respectively. The total frame numbers of the training set and the testing set are 160492 and 31191 respectively. We also crawled 791 person images and 791 clothes images and made every video associated with a person image and a clothes image. Therefore, a sample in the dataset is composed of a video, a person image and a clothes image.
Specifically, the dataset contains the following folders:
In the Final phase, we provide the person-clothse-pose tuples. Each tuple is represented in the format of "video1 video2 video3", in which videox is the name of video. Given such tuple, participators must utilize the person image associated with video1, clothing image associated with video2, and pose sequence from video3 to synthesize the virtual try-on video. You can download the tuple file through the Google Drive and the Baidu Drive. The Baidu Drive extract password is p9pn.
For more details about the video virtual try-on algorithm and VVT, please refer to FW-GAN(ICCV2019).
For video virtual try on, we use two metrics for evaluation:
It is worth to note that, the evaluation metric for the Development phase is SSIM while the evaluation metric for the Final phase is AMT.
Specifically, during the Development phase, We measure the SSIM score between the synthesized frames and ground truth frames in the testing set. The due date for the Development phase is May 1, 2020. The TOP 10 participators will be invited to attend the Final phase.
During the Final phase, we shuffle the testing set to ensure the person image is different from the person in the associated video and clothing image is different from the clothing in the associated person image. Then we provide the shuffled person-clothes-pose tuples and the participators are asked to synthesize virtual try-on videos according to these tuples. We will pick the TOP 10 participators of the Development phase to participate in the AMT evaluation. The 10 participators must upload the new results for AMT evaluation before May 20. 2020. We will announce the AMT scores before May 30, 2020.
A folder named vtryon_result.zip (Click to download a template file) contains your synthesized virtual try-on video frames with .png format. The number of synthesized video and the number of synthesized frames in each video folder should be the same of our testing set. Make sure these and then package the folder with zip format. Submit your results.zip and wait to see your rank.
Haoye Dong, Xiaodan Liang, XiaohuiShen, Bowen Wu, Bing-Cheng Chen, and Jian Yin. Flow-navigated warping gan for video virtual try-on. InICCV,pages 9206–9035, 2019.
For more information, please concate us at firstname.lastname@example.org or email@example.com.
Start: Feb. 20, 2020, midnight
May 20, 2020, midnight
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