Pedro Figueirêdo

Pedro Figueirêdo, Avinash Paliwal, Nima Khademi Kalantari

In this paper, we propose an algorithm to interpolate between a pair of images of a dynamic scene. While in the past years significant progress in frame interpolation has been made, current approaches are not able to handle images with brightness and illumination changes, which are common even when the images are captured shortly apart. We propose to address this problem by taking advantage of the existing optical flow methods that are highly robust to the variations in the illumination. Specifically, using the bidirectional flows estimated using an existing pre-trained flow network, we predict the flows from an intermediate frame to the two input images. To do this, we propose to encode the bidirectional flows into a coordinate-based network, powered by a hypernetwork, to obtain a continuous representation of the flow across time. Once we obtain the estimated flows, we use them within an existing blending network to obtain the final intermediate frame. Through extensive experiments, we demonstrate that our approach is able to produce significantly better results than state-of-the-art frame interpolation algorithms.

Pedro Figueiredo, Qihao He, Nima Khademi Kalantari

In this paper, we present a neural path guiding method to aid with Monte Carlo (MC) integration in rendering. Existing neural methods utilize distribution representations that are either fast or expressive, but not both. We propose a simple, but effective, representation that is sufficiently expressive and reasonably fast. Specifically, we break down the 2D distribution over the directional domain into two 1D probability distribution functions (PDF). We propose to model each 1D PDF using a neural network that estimates the distribution at a set of discrete coordinates. The PDF at an arbitrary location can then be evaluated and sampled through interpolation. To train the network, we maximize the similarity of the learned and target distributions. To reduce the variance of the gradient during optimizations and estimate the normalization factor, we propose to cache the incoming radiance using an additional network. Through extensive experiments, we demonstrate that our approach is better than the existing methods, particularly in challenging scenes with complex light transport.