Using a Graphics Turing Test to Evaluate the Effect of Frame Rate and Motion Blur on Telepresence of Animated Objects

A limited Graphics Turing Test is used to determine the frame rate that is required to achieve telepresence of an animated object. For low object velocities of 2.25 and 4.5 degrees of visual angle per second at 60 frames per second a rotating object with no added motion blur is able to pass the test. The results of the experiments confirm previous results in psychophysics and show that the Graphics Turing Test is a useful tool in computer graphics. Even with simulated motion blur, our Graphics Turing Test could not be passed with frame rates of 30 and 20 frames per second. Our results suggest that 60 frames per second (instead of 30 frames per second) should be considered the minimum frame rate to achieve object telepresence and that motion blur provides only limited benefits.</p

Practical Implementation of a Graphics Turing Test

We present a practical implementation of a variation of the Turing Test for realistic computer graphics. The test determines whether virtual representations of objects appear as real as genuine objects. Two experiments were conducted wherein a real object and a similar virtual object is presented to test subjects under specific restrictions. A criterion for passing the test is presented based on the probability for the subjects to be unable to recognise a computer generated object as virtual. The experiments show that the specific setup can be used to determine the quality of virtual reality graphics. Based on the results from these experiments, future versions of the Graphics Turing Test could ease the restrictions currently necessary in order to test object telepresence under more general conditions. Furthermore, the test could be used to determine the minimum requirements to achieve object telepresence.</p

Technological prerequisites for indistinguishability of a person and his/her computer replica

Some people wrongly believe that A. Turing’s works that underlie all modern computer science never discussed “physical” robots. This is not so, since Turing did speak about such machines, though making a reservation that this discussion was still premature. In particular, in his 1948 report [8], he suggested that a physical intelligent machine equipped with motors, cameras and loudspeakers, when wandering through the fields of England, would present “the danger to the ordinary citizen would be serious.” [8, ]. Due to this imperfection of technology in the field of knowledge that we now call robotics, the methodology that he proposed was based on human speech, or rather on text. Other natural human skills were too difficult to implement, while the exchange of cues via written messages was much more accessible for engineering implementation in Turing’s time. Nevertheless, since then, the progress of computer technology has taken forms that the founder of artificial intelligence could not have foreseen

Playing Smart - Artificial Intelligence in Computer Games

Abstract: With this document we will present an overview of artificial intelligence in general and artificial intelligence in the context of its use in modern computer games in particular. To this end we will firstly provide an introduction to the terminology of artificial intelligence, followed by a brief history of this field of computer science and finally we will discuss the impact which this science has had on the development of computer games. This will be further illustrated by a number of case studies, looking at how artificially intelligent behaviour has been achieved in selected games

Virtual Rephotography: Novel View Prediction Error for 3D Reconstruction

The ultimate goal of many image-based modeling systems is to render photo-realistic novel views of a scene without visible artifacts. Existing evaluation metrics and benchmarks focus mainly on the geometric accuracy of the reconstructed model, which is, however, a poor predictor of visual accuracy. Furthermore, using only geometric accuracy by itself does not allow evaluating systems that either lack a geometric scene representation or utilize coarse proxy geometry. Examples include light field or image-based rendering systems. We propose a unified evaluation approach based on novel view prediction error that is able to analyze the visual quality of any method that can render novel views from input images. One of the key advantages of this approach is that it does not require ground truth geometry. This dramatically simplifies the creation of test datasets and benchmarks. It also allows us to evaluate the quality of an unknown scene during the acquisition and reconstruction process, which is useful for acquisition planning. We evaluate our approach on a range of methods including standard geometry-plus-texture pipelines as well as image-based rendering techniques, compare it to existing geometry-based benchmarks, and demonstrate its utility for a range of use cases.Comment: 10 pages, 12 figures, paper was submitted to ACM Transactions on Graphics for revie

PixColor: Pixel Recursive Colorization

We propose a novel approach to automatically produce multiple colorized versions of a grayscale image. Our method results from the observation that the task of automated colorization is relatively easy given a low-resolution version of the color image. We first train a conditional PixelCNN to generate a low resolution color for a given grayscale image. Then, given the generated low-resolution color image and the original grayscale image as inputs, we train a second CNN to generate a high-resolution colorization of an image. We demonstrate that our approach produces more diverse and plausible colorizations than existing methods, as judged by human raters in a "Visual Turing Test"