Improving VIP Viewer Gaze Estimation and Engagement Using Adaptive Dynamic Anamorphosis
Anamorphosis for 2D displays can provide viewer centric perspective viewing, enabling 3D appearance, eye contact and engagement, by adapting dynamically in real time to a single moving viewer’s viewpoint, but at the cost of distorted viewing for other viewers. We present a method for constructing non-linear projections as a combination of anamorphic rendering of selective objects whilst reverting to normal perspective rendering of the rest of the scene. Our study defines a scene consisting of five characters, with one of these characters selectively rendered in anamorphic perspective.
Denoising with Kernel Prediction and Asymmetric Loss Functions
We present a modular convolutional architecture for denoising rendered images.
Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks
We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks.
Reversible Jump Metropolis Light Transport using Inverse Mappings
We investigate how to generalize this concept to non-invertible sampling techniques commonly found in practice, and introduce probabilistic inverses that extend our perturbation to cover most sampling methods found in light transport simulations.
IRIDiuM+: Deep Media Storytelling with Non-Linear Light Field Video
The interactive narrative guides guests through the immersive story with lighting and spatial audio design and integrates both walkable and air haptic actuators.
Spectral and Decomposition Tracking for Rendering Heterogeneous Volumes
We present two novel unbiased techniques for sampling free paths in heterogeneous participating media.
Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings
We introduce a deep learning approach for denoising Monte Carlo-rendered images that produces high-quality results suitable for production.
Efficient Rendering of Heterogeneous Poly-Disperse Granular Media
We address the challenge of efficiently rendering massive assemblies of grains within a forward path-tracing framework.
Image-Space Control Variates for Rendering
We propose an image-space (iterative) reconstruction scheme that employs control variates to reduce variance.
Pixel History Linear Models for Real-Time Temporal Filtering
We propose a new real-time temporal filtering and antialiasing (AA) method for rasterization graphics pipelines.
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