[1] Abdal, R., Zhu, P., Mitra, N.J., Wonka, P., 2020. Styleflow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. CoRR abs/2008.02401. URL: https://arxiv.org/abs/2008.02401, arXiv:2008.02401.
[2] Alaluf, Y., Patashnik, O., Cohen-Or, D., 2021. Only a matter of style: Age transformation using a style-based regression model. CoRR abs/2102.02754. URL: https://arxiv.org/abs/2102.02754, arXiv:2102.02754.
[3] An, S., Xu, H., Shi, Y., Song, G., Ogras, U.Y., Luo, L., 2023. Panohead: Geometry-aware 3d full-head synthesis in 360deg, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 20950-20959.
[4] Bhattachad, A., Forsyth, D., 2023. Stylitgan: Prompting stylegan to generate new illumination conditions, in: arXiv.
[5] Blanz, V., Vetter, T., 1999. A morphable model for the synthesis of 3d faces, in: Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pp. 187-194.
[6] Cascante-Bonilla, P., Shehada, K., Smith, J.S., Doveh, S., Kim, D., Panda, R., Varol, G., Oliva, A., Ordonez, V., Feris, R., Karlinsky, L., 2023. Going beyond nouns with vision & language models using synthetic data. arXiv:2303.17590.
[7] Chan, E., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G., 2020. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis, in: arXiv.
[8] Chan, E.R., Lin, C.Z., Chan, M.A., Nagano, K., Pan, B., Mello, S.D., Gallo, O., Guibas, L.J., Tremblay, J., Khamis, S., Karras, T., Wetzstein, G., 2021. Efficient geometry-aware 3d generative adversarial networks. CoRR abs/2112.07945. URL: https://arxiv.org/abs/2112.07945, arXiv:2112.07945.
[9] Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F., 2015. Shapenet: An information-rich 3d model repository. arXiv:1512.03012.
[10] Chen, H., Tretschk, E., Stuyck, T., Kadlecek, P., Kavan, L., Vouga, E., Lassner, C., 2022. Virtual elastic objects. CoRR abs/2201.04623. URL: https://arxiv.org/abs/2201.04623, arXiv:2201.04623.
[11] Choi, Y., Uh, Y., Yoo, J., Ha, J.W., 2020. Stargan v2: Diverse image synthesis for multiple domains, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
[12] Cohen-Bar, D., Richardson, E., Metzer, G., Giryes, R., Cohen-Or, D., 2023. Set-the-scene: Global-local training for generating controllable nef scenes. arXiv:2303.13450.
[13] Crowson, K., Biderman, S., Kornis, D., Stander, D., Hallahan, E., Castricato, L., Raff, E., 2022. Vqgan-clip: Open domain image generation and editing with natural language guidance. arXiv:2204.08583.
[14] Deng, J., Guo, J., Xue, N., Zafeiriou, S., 2019a. Arcface: Additive angular margin loss for deep face recognition, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Deng, Y., Yang, J., Chen, D., Wen, F., Tong, X., 2020. Disentangled and controllable face image generation via 3d imitative-contrastive learning, in: IEEE Computer Vision and Pattern Recognition.
[16] Deng, Y., Yang, J., Xu, S., Chen, D., Jia, Y., Tong, X., 2019b. Accurate 3d face reconstruction with weakly-supervised learning: From single image to image set, in: IEEE Computer Vision and Pattern Recognition Workshops.
[17] Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y., 2014. Generative adversarial networks. arXiv:1406.2661.