SIGGRAPH 2022 Papers

1 Accuracy of Our Approximation 1.1 Clamped Cosine Fig. 1 shows the error of our spherical Gaussian (SG) approximation for clamped cosine. Our approximation balances the approximation error and numerical error to reduce the sum of them. Even without using Kahan’s precise computation [Kahan(2004)] for Eq. 3 in the main document, the maximum numerical error is still almost the same as the maximum approximation error. Difference

In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or stylization of a content image from a certain domain), our method has no such restriction. In addition, we implement an improved version that can generate a wide range of results with varying degrees of detail, style and structure, with a boost in generation speed. To further enhance the results, we insert an artistic super-resolution module in the generative pipeline. This module will bring additional details such as patterns specific to painters, slight brush marks, and so on.

When navigating within an unfamiliar virtual environment in VR, transitions between pre-defined viewpoints are known to facilitate spatial awareness of a user. Previously, different viewpoint transition techniques had been investigated, but mainly for single-scale environments. We present a comparative study of zoom-in transition techniques, where the viewpoint of a user is being smoothly transitioned from a large level of scale (LoS) to a smaller LoS in a multiscale virtual environment (MVE) with a nested structure. We identify that orbiting first before zooming in is preferred over other alternatives when transitioning to a viewpoint at a small LoS.