DeepFashion-MultiModal

Text2Human: Text-Driven Controllable Human Image Generation
Yuming Jiang, Shuai Yang, Haonan Qiu, Wayne Wu, Chen Change Loy and Ziwei Liu
In ACM Transactions on Graphics (Proceedings of SIGGRAPH), 2022.

From MMLab@NTU affliated with S-Lab, Nanyang Technological University and SenseTime Research.

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

DeepFashion-MultiModal is a large-scale high-quality human dataset with rich multi-modal annotations. It has the following properties:

1. It contains 44,096 high-resolution human images, including 12,701 full body human images.
2. For each full body images, we manually annotate the human parsing labels of 24 classes.
3. For each full body images, we manually annotate the keypoints.
4. We extract DensePose for each human image.
5. Each image is manually annotated with attributes for both clothes shapes and textures.
6. We provide a textual description for each image.

DeepFashion-MultiModal can be applied to text-driven human image generation, text-guided human image manipulation, skeleton-guided human image generation, human pose estimation, human image captioning, multi-modal learning for human images, human attribute recognition, human parsing prediction, and etc. The dataset is proposed in Text2Human.

Download Links

You can download using the following links:

Path	Size	Files	Format	Description
DeepFashion-MultiModal	~12 GB	-		main folder
├  image	~5.4 GB	44,096	JPG	images from DeepFashion of size 750×1101
├  parsing	~90 MB	12,701	PNG	manually annotated parsing labels
├  keypoints	956KB	2	TXT	manually annotated keypoints
├  DensePose	~5.6 GB	44,096	PNG	extracted DensePose
├  labels	575KB	3	TXT	three texts for shape, fabric, and color annotations
├  textual descriptions	~11 MB	1	JSON	textual descriptions for each image

Human Parsing Label

• Mask labels are defined as follows:

Label list
0: 'background'	1: 'top'	2: 'outer'	3: 'skirt'
4: 'dress'	5: 'pants'	6: 'leggings'	7: 'headwear'
8: 'eyeglass'	9: 'neckwear'	10: 'belt'	11: 'footwear'
12: 'bag'	13: 'hair'	14: 'face'	15: 'skin'
16: 'ring'	17: 'wrist wearing'	18: 'socks'	19: 'gloves'
20: 'necklace'	21: 'rompers'	22: 'earrings'	23: 'tie'

• You can read the labels using the following code:

from PIL import Image
import numpy as np

segm = Image.open(f)
segm = np.array(segm) # shape: [750, 1101]

Keypoints

• For each human image, we provide 21 keypoints. The keypoints are defined as follows:

• The keypoints_loc.txt file contains the coordinates of the keypoints. The format is as follows:

<img name> <x_1> <y_1> <x_2> <y_1> ... <x_21> <y_21>

  If the keypoints are not present, the keypoint is (-1, -1).

• The keypoints_vis.txt file indicates the visibility of the keypoints. The format is as follows:

<img name> <v_1> <v_2> ... <v_21>

  If the keypoint is visible, the value is 0. If the keypoint is present but hidden by other parts, the value is 1. If the keypoint is not present, the value is 2.

DensePose

• We extract DensePose using this repo. Please refer to this repo for more details.

Labels

Shape Annotations

• The definitions of shape annotations:

  0. sleeve length: 0 sleeveless, 1 short-sleeve, 2 medium-sleeve, 3 long-sleeve, 4 not long-sleeve, 5 NA
  1. lower clothing length: 0 three-point, 1 medium short, 2 three-quarter, 3 long, 4 NA
  2. socks: 0 no, 1 socks, 2 leggings, 3 NA
  3. hat: 0 no, 1 yes, 2 NA
  4. glasses: 0 no, 1 eyeglasses, 2 sunglasses, 3 have a glasses in hand or clothes, 4 NA
  5. neckwear: 0 no, 1 yes, 2 NA
  6. wrist wearing: 0 no, 1 yes, 2 NA
  7. ring: 0 no, 1 yes, 2 NA
  8. waist accessories: 0 no, 1 belt, 2 have a clothing, 3 hidden, 4 NA
  9. neckline: 0 V-shape, 1 square, 2 round, 3 standing, 4 lapel, 5 suspenders, 6 NA
  10. outer clothing a cardigan?: 0 yes, 1 no, 2 NA
  11. upper clothing covering navel: 0 no, 1 yes, 2 NA

  Note: 'NA' means the relevant part is not visible.

• The format of shape annotations:

  <img_name> <shape_0> <shape_1> ... <shape_11>

Fabric Annotations

• The definitions of fabric annotations:

  0 denim, 1 cotton, 2 leather, 3 furry, 4 knitted, 5 chiffon, 6 other, 7 NA

  Note: 'NA' means the relevant part is not visible.

• The format of fabric annotations:

  <img_name> <upper_fabric> <lower_fabric> <outer_fabric>

Color Annotations

• The definitions of color annotations:

  0 floral, 1 graphic, 2 striped, 3 pure color, 4 lattice, 5 other, 6 color block, 7 NA

  Note: 'NA' means the relevant part is not visible.

• The format of color annotations:

  <img_name> <upper_color> <lower_color> <outer_color>

Papers using our dataset

• (SIGGRAPH 2022) Text2Human: Text-Driven Controllable Human Image Generation, Yuming Jiang et al. [Paper], [Code]
• (arXiv 2022) StyleGAN-Human: A Data-Centric Odyssey of Human Generation, Jianglin Fu et al. [Paper], [Code], [Project Page]

Related Datasets

• CelebA-Dialog ⇒ [Website]
  CelebA-Dialog is a large-scale visual-language face dataset. It has two properties:
  (1) Facial images are annotated with rich fine-grained labels, which classify one attribute into multiple degrees according to its semantic meaning.
  (2) Accompanied with each image, there are captions describing the attributes and a user request sample.

  • Detailed information (Images & Text Descriptions):
    • Number of identities: 10,177
    • Number of images: 202,599
    • 5 fine-grained attributes annotations per image: Bangs, Eyeglasses, Beard, Smiling, and Age

• DeepFashion ⇒ [Website]
  DeepFashion is a clothes database, which has several appealing properties:
  (1) DeepFashion contains over 800,000 diverse fashion images ranging from well-posed shop images to unconstrained consumer photos, constituting the largest visual fashion analysis database.
  (2) DeepFashion is annotated with rich information of clothing items. Each image in this dataset is labeled with 50 categories, 1,000 descriptive attributes, bounding box and clothing landmarks.
  (3) DeepFashion contains over 300,000 cross-pose/cross-domain image pairs.

Agreement

• The DeepFashion-MultiModal 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 DeepFashion-MultiModal 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:

@article{jiang2022text2human,
  title={Text2Human: Text-Driven Controllable Human Image Generation},
  author={Jiang, Yuming and Yang, Shuai and Qiu, Haonan and Wu, Wayne and Loy, Chen Change and Liu, Ziwei},
  journal={ACM Transactions on Graphics (TOG)},
  volume={41},
  number={4},
  articleno={162},
  pages={1--11},
  year={2022},
  publisher={ACM New York, NY, USA},
  doi={10.1145/3528223.3530104},
}

@inproceedings{liuLQWTcvpr16DeepFashion,
 author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou},
 title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
 booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 month = {June},
 year = {2016}
 }