[36] Orly Liba, Longqi Cai, Yun-Ta Tsai, Elad Eban, Yair Movshovitz-Attias, Yael Pritch, Huizhong Chen, and Jonathan T. Barron. Sky Optimization: Semantically aware image processing of skies in low-light photography. In CVPR Workshops, June 2020.
[37] Andrew Liu, Richard Tucker, Varun Jampani, Ameesh Makadia, Noah Snavely, and Angjoo Kanazawa. Infinite Nature: Perpetual view generation of natural scenes from a single image. In ICCV, 2021.
[38] Ao Luo, Fan Yang, Kunming Luo, Xin Li, Haoqiang Fan, and Shuaicheng Liu. Learning optical flow with adaptive graph reasoning. In AAAI, pages 1890-1898, 2022.
[39] Zhaoyang Lv, Kihwan Kim, Alejandro Troccoli, Deqing Sun, James M. Rehg, and Jan Kautz. Learning rigidity in dynamic scenes with a moving camera for 3D motion field estimation. In ECCV, pages 468-484, 2018.
[40] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In CVPR, 2016.
[41] John McCormac, Ankur Handa, Stefan Leutenegger, and Andrew J. Davison. SceneNet RGB-D: Can 5M synthetic images beat generic imagenet pre-training on indoor segmentation? In ICCV, 2017.
[42] Chenlin Meng, Ruiqi Gao, Diederik P. Kingma, Stefano Ermon, Jonathan Ho, and Tim Salimans. On distillation of guided diffusion models. In NeurIPS 2022 Workshop on Score-Based Methods, 2022.
[43] Moritz Menze, Christian Heipke, and Andreas Geiger. Joint 3D estimation of vehicles and scene flow. In ISPRS Workshop on Image Sequence Analysis (ISA), 2015.
[44] Moritz Menze, Christian Heipke, and Andreas Geiger. Object scene flow. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS), 2018.
[45] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In ICML, pages 8162-8171, 2021.
[46] Jia Ning, Chen Li, Zheng Zhang, Zigang Geng, Qi Dai, Kun He, and Han Hu. All in Tokens: Unifying output space of visual tasks via soft token. In ICCV, 2023.
[47] Ethan Perez, Florian Strub, Harm de Vries, Vincent Dumoulin, and Aaron C. Courville. FiLm: Visual reasoning with a general conditioning layer. In AAAI, 2018.
[48] Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image generation with clip latents. arXiv:2204.06125, 2022.
[49] René Ranftl, Katrin Lasinger, David Hafner, Konrad Schindler, and Vladlen Koltun. Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE T-PAMI, 44(3):1623-1637, 2020.
[50] René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun. Vision transformers for dense prediction. In ICCV, pages 12179-12188, 2021.
[51] Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, and Wojciech Zaremba. Sequence level training with recurrent neural networks. In ICLR, 2016.
[52] Zhongzheng Ren and Yong Jae Lee. Cross-Domain self-supervised multi-task feature learning using synthetic imagery. In CVPR, 2018.
[53] Stephan R. Richter, Zeeshan Hayder, and Vladlen Koltun. Playing for benchmarks. In ICCV, pages 2213-2222, 2017.
[54] Chitwan Sahara, William Chan, Huiwen Chang, Chris A. Lee, Jonathan Ho, Tim Salimans, David J. Fleet, and Mohammad Norouzi. Palette: Image-to-Image Diffusion Models. In SIGGRAPH, 2022.