With only two training views on real-world images, DS-NeRF significantly outperforms NeRF as well as other sparse-view variants.
We present a framework for automatically reconfiguring images of street scenes by populating, depopulating, or repopulating them with objects such as pedestrians or vehicles.
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment.
Reduction in the cost of Network Cameras along with a rise in connectivity enables entities all around the world to deploy vast arrays of camera networks.
We introduce the problem of perpetual view generation - long-range generation of novel views corresponding to an arbitrarily long camera trajectory given a single image.
We propose a learning-based framework for disentangling outdoor scenes into temporally-varying illumination and permanent scene factors.
In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs.