Ricardo Martin-Brualla

TL;DR: A learning-based method for synthesizing novel views of complex scenes using only unstructured collections of in-the-wild photographs, and applies it to internet photo collections of famous landmarks, to demonstrate temporally consistent novel view renderings that are significantly closer to photorealism than the prior state of the art.

TL;DR: A method that synthesizes novel views of complex scenes by interpolating a sparse set of nearby views using a network architecture that includes a multilayer perceptron and a ray transformer that estimates radiance and volume density at continuous 5D locations.

TL;DR: Neural rendering as discussed by the authors is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training.

TL;DR: This work applies traditional 3D reconstruction to register the photos and approximate the scene as a point cloud from Internet photos of a tourist landmark, and trains a deep neural network to learn the mapping of these initial renderings to the actual photos.

TL;DR: This state‐of‐the‐art report summarizes the recent trends and applications of neural rendering and focuses on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs.