Abstract
We introduce an automated pipeline for synthesizing texture maps in complex indoor scenes. With a style sample or color palette as inputs, our pipeline predicts theme color for each room using a GAN-based method, before generating texture maps using combinatorial optimization. We consider constraints on material selection, color correlation, and color palette matching. Our experiments show the pipeline’s ability to produce pleasing and harmonious textures for diverse layouts and our contribution of an interior furniture texture dataset with 4,337 texture images.
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References
Chen, G., Li, G., Nie, Y., Xian, C., Mao, A.: Stylistic indoor colour design via Bayesian network. Comput. Graph. 60, 34–45 (2016). https://doi.org/10.1016/j.cag.2016.08.009
Chen, K., Xu, K., Yu, Y., Wang, T., Hu, S.: Magic decorator: automatic material suggestion for indoor digital scenes. ACM Trans. Graph. 34(6), 232:1–232:11 (2015). https://doi.org/10.1145/2816795.2818096
Cho, J., Yun, S., Lee, K., Choi, J.Y.: Palettenet: image recolorization with given color palette. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 1058–1066. IEEE Computer Society (2017). https://doi.org/10.1109/CVPRW.2017.143
Fisher, M., Ritchie, D., Savva, M., Funkhouser, T., Hanrahan, P.: Example-based synthesis of 3D object arrangements. ACM Trans. Graph. (TOG) 31(6), 135 (2012)
Fu, H., et al.: 3D-front: 3D furnished rooms with layouts and semantics. In: 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, 10–17 October 2021, pp. 10913–10922. IEEE (2021). https://doi.org/10.1109/ICCV48922.2021.01075
Fu, Q., Chen, X., Wang, X., Wen, S., Zhou, B., Fu, H.: Adaptive synthesis of indoor scenes via activity-associated object relation graphs. ACM Trans. Graph. (TOG) 36(6), 201 (2017)
Fu, Q., Yan, H., Fu, H., Li, X.: Interactive design and preview of colored snapshots of indoor scenes. Comput. Graph. Forum 39(7), 543–552 (2020). https://doi.org/10.1111/cgf.14166
Goodfellow, I.J., et al.: Generative adversarial networks. CoRR abs/1406.2661 (2014). http://arxiv.org/abs/1406.2661
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein GANs. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Long Beach, CA, USA, 4–9 December 2017, pp. 5767–5777 (2017). https://proceedings.neurips.cc/paper/2017/hash/892c3b1c6dccd52936e27cbd0ff683d6-Abstract.html
Guo, Z., Guo, D., Zheng, H., Gu, Z., Zheng, B., Dong, J.: Image harmonization with transformer. In: 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, 10–17 October 2021, pp. 14850–14859. IEEE (2021). https://doi.org/10.1109/ICCV48922.2021.01460
Huang, H., Zhang, S., Martin, R.R., Hu, S.: Learning natural colors for image recoloring. Comput. Graph. Forum 33(7), 299–308 (2014). https://doi.org/10.1111/cgf.12498
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Conference Track Proceedings (2015). http://arxiv.org/abs/1412.6980
Liang, Y., Zhang, S.-H., Martin, R.R.: Automatic data-driven room design generation. In: Chang, J., Zhang, J.J., Magnenat Thalmann, N., Hu, S.-M., Tong, R., Wang, W. (eds.) AniNex 2017. LNCS, vol. 10582, pp. 133–148. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69487-0_10
Lin, S., Ritchie, D., Fisher, M., Hanrahan, P.: Probabilistic color-by-numbers: suggesting pattern colorizations using factor graphs. ACM Trans. Graph. 32(4), 37:1–37:12 (2013). https://doi.org/10.1145/2461912.2461988
Lu, X., Huang, S., Niu, L., Cong, W., Zhang, L.: Deep video harmonization with color mapping consistency. In: Raedt, L.D. (ed.) Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23–29 July 2022, pp. 1232–1238. ijcai.org (2022). https://doi.org/10.24963/ijcai.2022/172
Phan, H.Q., Fu, H., Chan, A.B.: Color orchestra: ordering color palettes for interpolation and prediction. IEEE Trans. Vis. Comput. Graph. 24(6), 1942–1955 (2018). https://doi.org/10.1109/TVCG.2017.2697948
Qi, S., Zhu, Y., Huang, S., Jiang, C., Zhu, S.C.: Human-centric indoor scene synthesis using stochastic grammar. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5899–5908 (2018)
Tsai, Y., Shen, X., Lin, Z., Sunkavalli, K., Lu, X., Yang, M.: Deep image harmonization. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, 21–26 July 2017, pp. 2799–2807. IEEE Computer Society (2017). https://doi.org/10.1109/CVPR.2017.299
Wang, B., Yu, Y., Wong, T., Chen, C., Xu, Y.: Data-driven image color theme enhancement. ACM Trans. Graph. 29(6), 146 (2010). https://doi.org/10.1145/1882261.1866172
Wang, B., Yu, Y., Xu, Y.: Example-based image color and tone style enhancement. ACM Trans. Graph. 30(4), 64 (2011). https://doi.org/10.1145/2010324.1964959
Weiss, T., Litteneker, A., Duncan, N., Nakada, M., Jiang, C., Yu, L.F., Terzopoulos, D.: Fast and scalable position-based layout synthesis. arXiv preprint arXiv:1809.10526 (2018)
Yu, L.F., Yeung, S.K., Tang, C.K., Terzopoulos, D., Chan, T.F., Osher, S.: Make it home: automatic optimization of furniture arrangement. ACM Trans. Graph. 30(4), 86 (2011)
Zhang, S., Zhang, S., Xie, W., Luo, C., Yang, Y., Fu, H.: Fast 3D indoor scene synthesis by learning spatial relation priors of objects. IEEE Trans. Vis. Comput. Graph. 28(9), 3082–3092 (2022). https://doi.org/10.1109/TVCG.2021.3050143
Zheng, J., Zhang, J., Li, J., Tang, R., Gao, S., Zhou, Z.: Structured3D: a large photo-realistic dataset for structured 3D modeling. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12354, pp. 519–535. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58545-7_30
Zhu, J., Guo, Y., Ma, H.: A data-driven approach for furniture and indoor scene colorization. IEEE Trans. Vis. Comput. Graph. 24(9), 2473–2486 (2018). https://doi.org/10.1109/TVCG.2017.2753255
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He, Y., Jin, YH., Liu, YT., Lu, BL., Yu, G. (2024). Color-Correlated Texture Synthesis for Hybrid Indoor Scenes. In: Hu, SM., Cai, Y., Rosin, P. (eds) Computer-Aided Design and Computer Graphics. CADGraphics 2023. Lecture Notes in Computer Science, vol 14250. Springer, Singapore. https://doi.org/10.1007/978-981-99-9666-7_14
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