he idea of computers generating content has been around since the 1950s. Some of the earliest attempts were focused on replicating human creativity by having computers generate visual art and music 1 . Unlike today's synthesized media, computer-generated content from the early era was far from realistic and easily distinguishable from that created by humans. It has taken decades and major leaps in artificial intelligence (AI) for generated content to reach a high level of realism.Generative and discriminative models are two different approaches to machines learning from data. Although discriminative models can identify a person in an image, generative models can produce a new image of a person that has never existed before. Recent leaps in generative models include generative adversarial networks (GANs) 2 . Since their introduction, models for AI-generated media, such as GANs, have enabled the hyper-realistic synthesis of digital content, including the generation of photorealistic images, cloning of voices, animation of faces and translation of images from one form to another 3-6 . The GAN architecture includes two neural networks, a generator and a discriminator. The generator is responsible for generating new content that resembles the input data, while the discriminator's job is to differentiate the generated or fake output from the real data. The two networks compete and try to outperform each other in a closed-feedback loop, resulting in a gradual increase of the realism of the generated output.GAN architectures can generate images of things that have never existed before, such as human faces 3,4 . However, StyleGAN is an example of a modifiable GAN that enables intuitive control of the facial details of generated images by separating high-level attributes like the identity of a person from low-level features such as hair or freckles, with few visible artefacts 4 . Researchers have also proposed an in-domain GAN inversion approach to enable the editing of GAN-generated images, allowing for de-aging or the addition of new facial expressions to existing photographs 7 . Meanwhile, transformers such as the ones used in the massive generative GPT-3 language model are already being shown to be successful for text-to-image generation 8 .
We present SoftAR, a novel spatial augmented reality (AR) technique based on a pseudo-haptics mechanism that visually manipulates the sense of softness perceived by a user pushing a soft physical object. Considering the limitations of projection-based approaches that change only the surface appearance of a physical object, we propose two projection visual effects, i.e., surface deformation effect (SDE) and body appearance effect (BAE), on the basis of the observations of humans pushing physical objects. The SDE visualizes a two-dimensional deformation of the object surface with a controlled softness parameter, and BAE changes the color of the pushing hand. Through psychophysical experiments, we confirm that the SDE can manipulate softness perception such that the participant perceives significantly greater softness than the actual softness. Furthermore, fBAE, in which BAE is applied only for the finger area, significantly enhances manipulation of the perception of softness. We create a computational model that estimates perceived softness when SDE+fBAE is applied. We construct a prototype SoftAR system in which two application frameworks are implemented. The softness adjustment allows a user to adjust the softness parameter of a physical object, and the softness transfer allows the user to replace the softness with that of another object.
Food 3D printing enables the creation of customized food structures based on a person's individual needs. In this paper, we explore the use of food 3D printing to create perceptual illusions for controlling the level of perceived satiety given a defined amount of calories. We present FoodFab, a system that allows users to control their food intake through modifying a food's internal structure via two 3D printing parameters: infill pattern and infill density. In two experiments with a total of 30 participants, we studied the effect of these parameters on users' chewing time that is known to affect people's feeling of satiety. Our results show that we can indeed modify the chewing time by varying infill pattern and density, and thus control perceived satiety. Based on the results, we propose two computational models and integrate them into a user interface that simplifies the creation of personalized food structures.
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