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Image Generation: A Review

Neural Processing Letters volume 54, pages 4609–4646 (2022)Cite this article

Abstract

The creation of an image from another and from different types of data including text, scene graph, and object layout, is one of the very challenging tasks in computer vision. In addition, capturing images from different views for generating an object or a product can be exhaustive and expansive to do manually. Now, using deep learning and artificial intelligence techniques, the generation of new images from different type of data has become possible. For that, a significant effort has been devoted recently to develop image generation strategies with a great achievement. To that end, we present in this paper, to the best of the authors’ knowledge, the first comprehensive overview of existing image generation methods. Accordingly, a description of each image generation technique is performed based on the nature of the adopted algorithms, type of data used, and main objective. Moreover, each image generation category is discussed by presenting the proposed approaches. In addition, a presentation of existing image generation datasets is given. The evaluation metrics that are suitable for each image generation category are discussed and a comparison of the performance of existing solutions is provided to better inform the state-of-the-art and identify their limitations and strengths. Lastly, the current challenges that are facing this subject are presented.

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Notes

  1. https://www.shapenet.org.

  2. http://image-net.org/.

  3. http://cocodataset.org/#home.

  4. https://synthia-dataset.net.

  5. https://deepai.org/dataset/market-1501.

  6. http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html.

  7. http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html.

  8. https://www.cs.toronto.edu/~kriz/cifar.html.

  9. https://paperswithcode.com/dataset/coco-stuff.

  10. http://visualgenome.org/.

  11. https://www.tensorflow.org/datasets/catalog/caltech_birds2011?hl=en.

  12. https://www.tensorflow.org/datasets/catalog/oxford_flowers102?hl=en.

  13. https://www.tensorflow.org/datasets/catalog/mnist?hl=en.

  14. https://structured3d-dataset.org/.

  15. https://paperswithcode.com/dataset/brats-2016.

  16. https://paperswithcode.com/dataset/svhn.

  17. https://paperswithcode.com/dataset/office-home.

  18. https://paperswithcode.com/dataset/human3-6m.

  19. https://visualqa.org/.

  20. http://sysu-hcp.net/lip/.

  21. https://github.com/marcdemers/FIGR-8.

  22. https://github.com/brendenlake/omniglot.

  23. https://www.robots.ox.ac.uk/~vgg/data/dtd/.

  24. https://www.tensorflow.org/datasets/catalog/celeb_a_hq?hl=en.

  25. https://paperswithcode.com/dataset/fashion-gen.

  26. https://pgram.com/dataset/dukemtmc-reid/.

  27. http://www.socsci.ru.nl:8180/RaFD2/RaFD.

  28. http://mlg.ucd.ie/datasets/bbc.html.

  29. https://paperswithcode.com/dataset/visual-relationship-detection-dataset.

  30. http://vision.cs.utexas.edu/projects/finegrained/utzap50k/.

  31. https://paperswithcode.com/dataset/ffhq.

  32. https://github.com/bchao1/Anime-Face-Dataset.

  33. https://www.cityscapes-dataset.com/.

  34. https://cs.stanford.edu/~acoates/stl10/.

  35. https://www.robots.ox.ac.uk/~ vgg/data/vgg_face/.

  36. https://paperswithcode.com/dataset/lsun.

  37. https://paperswithcode.com/dataset/reddit-corpus.

  38. http://hamlyn.doc.ic.ac.uk/vision/.

  39. https://paperswithcode.com/dataset/gta5.

  40. https://paperswithcode.com/dataset/lfw.

  41. https://www.tensorflow.org/datasets/catalog/places365_small?hl=en.

  42. https://www.tensorflow.org/datasets/catalog/kitti?hl=en.

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  1. Department of Computer Science, Sidi Mohamed Ben Abdellah University, Fez, Morocco

    Mohamed Elasri & Hamid Tairi

  2. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Omar Elharrouss & Somaya Al-Maadeed

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Elasri, M., Elharrouss, O., Al-Maadeed, S. et al. Image Generation: A Review. Neural Process Lett 54, 4609–4646 (2022). https://doi.org/10.1007/s11063-022-10777-x

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