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Interactive Point-based Image Manipulation

Chinnakrishna Achary, Hitesh Bhosale, Sahil Tailor, Jinay Vora

Abstract


Interactive point-based image manipulation is a novel Artificial Intelligence (AI) approach aimed at augmenting user-control over digitally generated images. Its foundations concretely lie in the field of Generative Artificial Intelligence and Deep Learning (DL), often touted as technologies with a bright prospective future. We survey fundamentals of the forementioned disciplines and measure their development in the context of image synthesis. We proceed to study multitude contemporary options available to perform controllable image synthesis and comprehensively explore one of the latest applications capable of performing interactive point-based manipulation

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References


X. A. T. T. L. L. L. A. M. a. C. T. Pan, “Drag your gan: Interactive point-based manipulation on the generative image manifold.,” in ACM SIGGRAPH 2023 Conference Proceedings., 2023.

A. T. W. V. D. K. A. B. S. a. A. A. B. Creswell, “Generative adversarial networks: An overview.,” IEEE signal processing magazine, vol. 35, no. 1, pp. 53-65, 2018.

I. J. P.-A. M. M. B. X. D. W.-F. S. O. A. C. a. Y. B. Goodfellow, “Generative adversarial nets.,” Advances in neural information processing systems, vol. 27, 2014.

T. S. L. M. A. J. H. J. L. a. T. A. Karras, “Analyzing and improving the image quality of stylegan.,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition., 2020.

A. M. E. G. B. F. B. H.-P. S. P. P. M. Z. a. C. T. Tewari, “Stylerig: Rigging stylegan for 3d control over portrait images.,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition., 2020.

J. C. Y. Y. S. Z. S. B. D. D. Z. a. Q. C. Zhu, “Linkgan: Linking gan latents to pixels for controllable image synthesis.,” in Proceedings of the IEEE/CVF International Conference on Computer Vision., 2023.

H. K. K. D. L. S. W. K. A. T. a. S. F. Ling, “Editgan: High-precision semantic image editing.,” in Advances in Neural Information Processing Systems, 2021.

Y. Endo, “User‐Controllable Latent Transformer for StyleGAN Image Layout Editing.,” Computer Graphics Forum., vol. 41, no. 7, pp. 395-406, 2022.

A. P. D. A. N. C. C. a. M. C. Ramesh, “Hierarchical text-conditional image generation with clip latents.,” arXiv preprint arXiv:2204.06125, 2022.

Z. a. J. D. Teed, “Raft: Recurrent all-pairs field transforms for optical flow.,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, 2020.

A. W. Z. F. a. K. F. Harley, “Particle video revisited: Tracking through occlusions using point trajectories.,” in European Conference on Computer Vision., 2022.

R. P. I. A. A. E. E. S. a. O. W. Zhang, “The unreasonable effectiveness of deep features as a perceptual metric.,” in Proceedings of the IEEE conference on computer vision and pattern recognition., 2018.


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