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Comprehending natural language instructions is a charming property for both 2D and 3D layout synthesis systems.
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K. Gupta, J. Lazarow, A. Achille, L. S. Davis, V. Mahadevan, and A. Shrivastava, “Layouttransformer: Layout generation and completion with self-attention,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 1004–1014
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M.-J. Yang, Y.-X. Guo, B. Zhou, and X. Tong, “Indoor scene generation from a collection of semantic-segmented depth images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 15 203–15 212
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D. Paschalidou, A. Kar, M. Shugrina, K. Kreis, A. Geiger, and S. Fidler, “Atiss: Autoregressive transformers for indoor scene synthesis,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 34, pp. 12 013–12 026, 2021
2021
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H. Dhamo, F. Manhardt, N. Navab, and F. Tombari, “Graph-to-3d: End-to-end generation and manipulation of 3d scenes using scene graphs,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 16 352–16 361
2021
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W. Para, P. Guerrero, T. Kelly, L. J. Guibas, and P. Wonka, “Generative layout modeling using constraint graphs,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 6690–6700
2021
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K. Kikuchi, E. Simo-Serra, M. Otani, and K. Yamaguchi, “Constrained graphic layout generation via latent optimization,” in Proceedings of the 29th ACM International Conference on Multimedia (ACM MM) , 2021
2021
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S. Garg, H. Dhamo, A. Farshad, S. Musatian, N. Navab, and F. Tombari, “Unconditional scene graph generation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 16 362–16 371
2021
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E. Hoogeboom, D. Nielsen, P. Jaini, P. Forré, and M. Welling, “Argmax flows and multinomial diffusion: Learning categorical distributions,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 34, pp. 12 454–12 465, 2021
2021
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J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. Van Den Berg, “Structured denoising diffusion models in discrete state-spaces,” Advances in Neural Information Processing Systems (NeurIPS) , vol. 34, pp. 17 981–17 993, 2021
2021
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A. Ramesh, M. Pavlov, G. Goh, S. Gray, C. Voss, A. Radford, M. Chen, and I. Sutskever, “Zero-shot text-to-image generation,” in International Conference on Machine Learning (ICML) , 2021, pp. 8821–8831
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2023
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H. Y. Hsu, X. He, Y. Peng, H. Kong, and Q. Zhang, “Posterlayout: A new benchmark and approach for content-aware visual-textual presentation layout,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 6018–6026
2023
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S. Chai, L. Zhuang, and F. Yan, “Layoutdm: Transformer-based diffusion model for layout generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023, pp. 18 349–18 358
2023
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J. Lin, M. Zhou, Y. Ma, Y. Gao, C. Fei, Y. Chen, Z. Yu, and T. Ge, “Autoposter: A highly automatic and content-aware design system for advertising poster generation,” in Proceedings of the 31st ACM International Conference on Multimedia (ACM MM) , 2023, pp. 1250–1260
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2024
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J. Tang, Y. Nie, L. Markhasin, A. Dai, J. Thies, and M. Nießner, “Diffuscene: Scene graph denoising diffusion probabilistic model for generative indoor scene synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
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