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Fashion-Text2Video

Text2Performer: Text-Driven Human Video Generation </br> Yuming Jiang, Shuai Yang, Tong Liang Koh, Wayne Wu, Chen Change Loy and Ziwei Liu </br> In International Conference on Computer Vision (ICCV), 2023.

From MMLab@NTU affliated with S-Lab, Nanyang Technological University and Shanghai AI Laboratory.

[Project Page] | [Paper] | [Code] | [Demo Video]

Fashion-Text2Video is a human video dataset with rich label and text annotations. It has the following properties:

1. It contains 600 high-resolution human videos from Fashion Dataset.
2. For each video, we have annotations for motions (labels and texts).
3. For each video, we have text descriptions for clothing textures and shapes.

Fashion-Text2Video dataset can be applied to text-driven human video generation. The dataset is proposed in Text2Performer.

Download Links

You can download using the following links:

Motion Texts

• The annotations for each video are in one txt files. The format of the annotations is as follows:

<start_frame_seg1> <end_frame_seg1> <text_seg1>
<start_frame_seg2> <end_frame_seg2> <text_seg2>
...

Motion Labels

• We provide the source motion labels, which are manually labelled. The annotations for each video are in one txt files. The format of the annotations is as follows:

<start_frame_seg1> <end_frame_seg1> <label_seg1>
<start_frame_seg2> <end_frame_seg2> <label_seg2>
...

You can also use the template to generate more diverse text descriptions.

Agreement

• The Fashion-Text2Video dataset is available for non-commercial research purposes only.
• You agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
• You agree not to further copy, publish or distribute any portion of the Fashion-Text2Video dataset. Except, for internal use at a single site within the same organization it is allowed to make copies of the dataset.

Citation

If you find this dataset useful for your research and use it in your work, please consider cite the following papers:

@inproceedings{jiang2023text2performer,
  title={Text2Performer: Text-Driven Human Video Generation},
  author={Jiang, Yuming and Yang, Shuai and Koh, Tong Liang and Wu, Wayne and Loy, Chen Change and Liu, Ziwei},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year={2023}
}