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TexControl: Sketch-Based Two-Stage Fashion Image Generation Using Diffusion Model

Published 7 May 2024 in cs.CV and cs.GR | (2405.04675v1)

Abstract: Deep learning-based sketch-to-clothing image generation provides the initial designs and inspiration in the fashion design processes. However, clothing generation from freehand drawing is challenging due to the sparse and ambiguous information from the drawn sketches. The current generation models may have difficulty generating detailed texture information. In this work, we propose TexControl, a sketch-based fashion generation framework that uses a two-stage pipeline to generate the fashion image corresponding to the sketch input. First, we adopt ControlNet to generate the fashion image from sketch and keep the image outline stable. Then, we use an image-to-image method to optimize the detailed textures of the generated images and obtain the final results. The evaluation results show that TexControl can generate fashion images with high-quality texture as fine-grained image generation.

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References (14)
  1. Y. He, H. Xie, and K. Miyata, “Sketch2cloth: Sketch-based 3d garment generation with unsigned distance fields,” arXiv preprint arXiv:2303.00167, 2023.
  2. A. Jain, D. Modi, R. Jikadra, and S. Chachra, “Text to image generation of fashion clothing,” in 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom).   IEEE, 2019, pp. 355–358.
  3. C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al., “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in Neural Information Processing Systems, vol. 35, pp. 36 479–36 494, 2022.
  4. A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125, 2022.
  5. R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10 684–10 695.
  6. L. Zhang, A. Rao, and M. Agrawala, “Adding conditional control to text-to-image diffusion models,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 3836–3847.
  7. J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning.   PMLR, 2015, pp. 2256–2265.
  8. Y. Song and S. Ermon, “Generative modeling by estimating gradients of the data distribution,” Advances in neural information processing systems, vol. 32, 2019.
  9. J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems, vol. 33, pp. 6840–6851, 2020.
  10. S. Luo, Y. Tan, L. Huang, J. Li, and H. Zhao, “Latent consistency models: Synthesizing high-resolution images with few-step inference,” arXiv preprint arXiv:2310.04378, 2023.
  11. Y. R. Cui, Q. Liu, C. Y. Gao, and Z. Su, “Fashiongan: display your fashion design using conditional generative adversarial nets,” in Computer Graphics Forum, vol. 37, no. 7.   Wiley Online Library, 2018, pp. 109–119.
  12. Y. Jiang, S. Yang, H. Qiu, W. Wu, C. C. Loy, and Z. Liu, “Text2human: Text-driven controllable human image generation,” ACM Transactions on Graphics (TOG), vol. 41, no. 4, pp. 1–11, 2022.
  13. A. Baldrati, D. Morelli, G. Cartella, M. Cornia, M. Bertini, and R. Cucchiara, “Multimodal garment designer: Human-centric latent diffusion models for fashion image editing,” arXiv preprint arXiv:2304.02051, 2023.
  14. M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” Advances in neural information processing systems, vol. 30, 2017.

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