[44] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021. 3, 6, 7, 8, 19, 20, 21, 22
[45] Ali Razavi, Aaron Van den Oord, and Oriol Vinyals. Generating diverse high-fidelity images with vq-vae-2. Advances in neural information processing systems, 32, 2019. 2
[46] Elad Richardson, Yuval Alaluf, Or Patashnik, Yotam Nitzan, Yaniv Azar, Stav Shapiro, and Daniel Cohen-Or. Encoding in style: a stylegan encoder for image-to-image translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2287–2296, 2021. 2
[47] Daniel Roich, Ron Mokady, Amit H Bermano, and Daniel Cohen-Or. Pivotal tuning for latent-based editing of real images. arXiv preprint arXiv:2106.05744, 2021. 2
[48] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10684-10695, 2022. 2
[49] Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016. 2
[50] Mert Bulent Sariyildiz, Karteek Alahari, Diane Larlus, and Yannis Kalantidis. Fake it till you make it: Learning transferable representations from synthetic imagenet clones. In CVPR 2023-IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. 3
[51] Lavanya Sharan, Ruth Rosenholtz, and Edward Adelson. Material perception: What can you see in a brief glance? Journal of Vision, 9(8):784-784, 2009. 10
[52] Yujun Shen, Ceyuan Yang, Xiaou Tang, and Bolei Zhou. Interfacegan: Interpreting the disentangled face representation learned by gans. IEEE transactions on pattern analysis and machine intelligence, 2020. 2
[53] Yujun Shen and Bolei Zhou. Closed-form factorization of latent semantics in gans. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 1532-1540, 2021. 2, 4
[54] Zifan Shi, Yujun Shen, Jiapeng Zhu, Dit-Yan Yeung, and Qifeng Chen. 3d-aware indoor scene synthesis with depth priors. 2022. 3
[55] Alon Shoshan, Nadav Bhonker, Igor Kviatkovsky, and Gerard Medioni. Gan-control: Explicitly controllable gans. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 14083-14093, 2021. 2
[56] Feitong Tan, Sean Fanello, Abhimitra Meka, Sergio Orts-Escalano, Danhang Tang, Rohit Pandey, Jonathan Taylor, Ping Tan, and Yinda Zhang. Volux-gan: A generative model for 3d face synthesis with hdri relighting. arXiv preprint arXiv:2201.04873, 2022. 3
[57] Christos Tzelepis, Georgios Tzimiropoulos, and Ioannis Patras. Warpedganspace: Finding nonlinear rbf paths in gan latent space. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 6393-6402, 2021. 2, 4
[58] Aaron Van Den Oord, Oriol Vinyals, et al. Neural discrete representation learning. Advances in neural information processing systems, 30, 2017. 2
[59] Vaibhav Vavilala, Seemandhar Jain, Rahul Vasanth, Anand Bhattachad, and David Forsyth. Blocks2world: Controlling realistic scenes with editable primitives. arXiv preprint arXiv:2307.03847, 2023. 2
[60] Andrey Voynov and Artem Babenko. Unsupervised discovery of interpretable directions in the gan latent space. In International conference on machine learning, pages 9786-9796. PMLR, 2020. 2
[61] Tengfei Wang, Yong Zhang, Yanbo Fan, Jue Wang, and Qifeng Chen. High-fidelity GAN inversion for image attribute editing. In CVPR, 2022. 2
[62] Zongze Wu, Dani Lischinski, and Eli Shechtman. Stylespace analysis: Disentangled controls for stylegan image generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12863-12872, 2021. 2, 3
[63] Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, and Shalini De Mello. Open-Vocabulary Panoptic Segmentation with Text-to-Image Diffusion Models. arXiv preprint arXiv:2303.04803, 2023. 3
[64] Ceyuan Yang, Yujun Shen, and Bolei Zhou. Semantic hierarchy emerges in deep generative representations for scene synthesis. International Journal of Computer Vision, 2020. 2
[65] Huiting Yang, Liangyu Chai, Qiang Wen, Shuang Zhao, Zixun Sun, and Shengfeng He. Discovering interpretable latent space directions of gans beyond binary attributes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12177-12185, 2021. 2, 4