[1] M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks. In Int. Conf. Mach. Learn., pages 214-223, 2017.
[2] Q. Bai, C. Yang, Y. Xu, X. Liu, Y. Yang, and Y. Shen. GLeaD: Improving GANs with a generator-leading task. In IEEE Conf. Comput. Vis. Pattern Recog., 2023.
[3] A. Brock, J. Donahue, and K. Simonyan. Large scale GAN training for high fidelity natural image synthesis. In Int. Conf. Learn. Represent., 2019.
[4] A. Brock, J. Donahue, and K. Simonyan. Large scale GAN training for high fidelity natural image synthesis. In Int. Conf. Learn. Represent., 2019.
[5] T. Brooks, J. Hellsten, M. Aittala, T.-C. Wang, T. Aila, J. Lehtinen, M.-Y. Liu, A. A. Efros, and T. Karras. Generating long videos of dynamic scenes. In Adv. Neural Inform. Process. Syst., 2022.
[6] E. R. Chan, M. Monteiro, P. Kellnhofer, J. Wu, and G. Wetzstein.  $\pi$ -GAN: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In IEEE Conf. Comput. Vis. Pattern Recog., 2021.
[7] E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis, et al. Efficient geometry-aware 3d generative adversarial networks. In IEEE Conf. Comput. Vis. Pattern Recog., 2022.
[8] A. Clark, J. Donahue, and K. Simonyan. Adversarial video generation on complex datasets. arXiv preprint arXiv:1907.06571, 2019.
[9] J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei. Deformable convolutional networks. In Int. Conf. Comput. Vis., 2017.
[10] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In IEEE Conf. Comput. Vis. Pattern Recog., 2009.
[11] G. Fox, A. Tewari, M. Elgharib, and C. Theobalt. StyleVideoGAN: A temporal generative model using a pretrained StyleGAN. In Brit. Mach. Vis. Conf., 2021.
[12] S. Ge, T. Hayes, H. Yang, X. Yin, G. Pang, D. Jacobs, J.-B. Huang, and D. Parikh. Long video generation with time-agnostic VQGAN and time-sensitive transformer. In Eur. Conf. Comput. Vis., 2022.
[13] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Adv. Neural Inform. Process. Syst., 2014.
[14] J. Gu, L. Liu, P. Wang, and C. Theobalt. Stylenerf: A style-based 3d-aware generator for high-resolution image synthesis. In Int. Conf. Learn. Represent., 2022.
[15] I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville. Improved training of wasserstein GANs. In Adv. Neural Inform. Process. Syst., 2017.
[16] Z. He, M. Kan, and S. Shan. EigenGAN: Layer-wise eigen-learning for GANs. In Int. Conf. Comput. Vis., 2021.
[17] 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. In Adv. Neural Inform. Process. Syst., 2017.
[18] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. Adv. Neural Inform. Process. Syst., 33:6840-6851, 2020.
[19] X. Huang and S. Belongie. Arbitrary style transfer in real-time with adaptive instance normalization. In Int. Conf. Comput. Vis., 2017.
[20] M. Jaderberg, K. Simonyan, A. Zisserman, et al. Spatial transformer networks. In Adv. Neural Inform. Process. Syst., 2015.