“DeepFovea: Neural Reconstruction for Foveated Rendering and Video Compression using Learned Natural Video Statistics”
Conference:
Type:
Entry Number: 58
Title:
- DeepFovea: Neural Reconstruction for Foveated Rendering and Video Compression using Learned Natural Video Statistics
Presenter(s)/Author(s):
Abstract:
Recent advances in head-mounted displays (HMDs) provide new levels of immersion by delivering imagery straight to human eyes. The high spatial and temporal resolution requirements of these displays pose a tremendous challenge for real-time rendering and video compression. Since the eyes rapidly decrease in spatial acuity with increasing eccentricity, providing high resolution to peripheral vision is unnecessary. Upcoming VR displays provide real-time estimation of gaze, enabling gaze-contingent rendering and compression methods that take advantage of this acuity falloff. In this setting, special care must be given to avoid visible artifacts such as a loss of contrast or addition of flicker.
References:
Martin Arjovsky, Soumith Chintala, and Léon Bottou. 2017. Wasserstein Generativen Adversarial Networks. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research), Doina Precup and Yee Whye Teh (Eds.), Vol. 70. PMLR, 214–223.
Brian Guenter, Mark Finch, Steven Drucker, Desney Tan, and John Snyder. 2012. Foveated 3D Graphics. ACM Transactions on Graphics (Proc. SIGGRAPH) 31, 6, Article 164 (2012), 164:1–164:10 pages.
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida. 2018. Spectral Normalization for Generative Adversarial Networks. CoRR abs/1802.05957 (2018).
O. Ronneberger, P. Fischer, and T. Brox. 2015. U-Net: Convolutional Networks for
Biomedical Image Segmentation. In Medical Image Computing and Computer-Assisted Intervention (MICCAI) (LNCS), Vol. 9351. 234–241.
Karen Simonyan and Andrew Zisserman. 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. CoRR abs/1409.1556 (2014).
