Generative adversarial networks (GANs) generate high-dimensional vector spaces (latent spaces) that can interchangeably represent vectors as images. Advancements have extended their ability to computationally generate images indistinguishable from real images such as faces, and more importantly, to manipulate images using their inherit vector values in the latent space. This interchangeability of latent vectors has the potential to calculate not only the distance in the latent space, but also the human perceptual and cognitive distance toward images, that is, how humans perceive and recognize images. However, it is still unclear how the distance in the latent space correlates with human perception and cognition. Our studies investigated the relationship between latent vectors and human perception or cognition through psycho-visual experiments that manipulates the latent vectors of face images. In the perception study, a change perception task was used to examine whether participants could perceive visual changes in face images before and after moving an arbitrary distance in the latent space. In the cognition study, a face recognition task was utilized to examine whether participants could recognize a face as the same, even after moving an arbitrary distance in the latent space. Our experiments show that the distance between face images in the latent space correlates with human perception and cognition for visual changes in face imagery, which can be modeled with a logistic function. By utilizing our methodology, it will be possible to interchangeably convert between the distance in the latent space and the metric of human perception and cognition, potentially leading to image processing that better reflects human perception and cognition.

Live instructors perspective videos are useful to present intuitive visual instructions for trainees in medical and industrial settings. In such videos, the instructors hands often hide the work area. In this demo, we present a diminished hand for visualizing the work area hidden by hands by capturing the work area with multiple cameras. To achieve the diminished reality, we use a light field rendering technique, in which light rays avoid passing through penalty points set in the unstructured light fields reconstructed from the multiple viewpoint images.

This paper proposes a remote welding robot manipulation system by using multi-view images. After an operator specifies two-dimensional path on images, the system transforms it into three-dimensional path and displays the movement of the robot by overlaying graphics with images. The accuracy of our system is sufficient to weld objects when combining with a sensor in the robot. The system allows the non-expert operator to weld objects remotely and intuitively, without the need to create a 3D model of a processed object beforehand.