Elastic Images: Perceiving Local Elasticity of Images Through a Novel Pseudo-Haptic Deformation Effect

International audienceWe introduce the Elastic Images, a novel pseudo-haptic feedback technique which enables the perception of the local elasticity of images without the need of any haptic device. The proposed approach focus on whether visual feedback is able to induce a sensation of stiffness when the user interacts with an image using a standard mouse. The user, when clicking on a Elastic Image, is able to deform it locally according to its elastic properties. To reinforce the effect, we also propose the generation of procedural shadows and creases to simulate the compressibility of the image and several mouse cursors replacements to enhance pressure and stiffness perception. A psychophysical experiment was conducted to quantify this novel pseudo-haptic perception and determine its perceptual threshold (or its Just Noticeable Difference). The results showed that users were able to recognize up to eight different stiffness values with our proposed method and confirmed that it provides a perceivable and exploitable sensation of elasticity. The potential applications of the proposed approach range from pressure sensing in product catalogs and games, or its usage in graphical user interfaces for increasing the expressiveness of widgets

A Human-Perceived Softness Measure of Virtual 3D Objects

We introduce the problem of computing a human-perceived softness measure for virtual 3D objects. As the virtual objects do not exist in the real world, we do not directly consider their physical properties but instead compute the human-perceived softness of the geometric shapes. In an initial experiment, we find that humans are highly consistent in their responses when given a pair of vertices on a 3D model and asked to select the vertex that they perceive to be more soft. This motivates us to take a crowdsourcing and machine learning framework. We collect crowdsourced data for such pairs of vertices. We then combine a learning-to-rank approach and a multi-layer neural network to learn a non-linear softness measure mapping any vertex to a softness value. For a new 3D shape, we can use the learned measure to compute the relative softness of every vertex on its surface. We demonstrate the robustness of our framework with a variety of 3D shapes and compare our non-linear learning approach with a linear method from previous work. Finally, we demonstrate the accuracy of our learned measure with user studies comparing our measure with the human-perceived softness of both virtual and real objects, and we show the usefulness of our measure with some applications

Perceptual Effects in Physically Based Animation with Rigid and Deformable Objects

We perform four psychophysical studies to investigate the perceptual effect of factors in the rendering and simulation stages of physically based animation production. Our study provides helpful insights in how to improve visual plausibility or reduce computational cost, which may allow artists to adjust their designs to enhance or minimize the perceived deformation in a model, or to choose a more efficient dynamics model and simpler mesh used in simulation without harming the visual plausibility. In our first study, we find that appearance can potentially influence people’s sensitivity to differences of deformation as well as subjective rating of softness. Further analysis shows that, in simple scenarios, the effect of low-level visual details in appearance can be dominant, even if high-level information delivered by appearance has the opposite implication. Another experiment shows that as the number of objects in a scenario increases, objects are perceived to be stiffer. In the second study, we quantitatively measure how different low-level visual details can influence people’s perceived stiffness of a deformable sphere under physically based simulation. We find that checkerboard pattern with certain combinations of spatial frequency and contrast can reduce the perceived stiffness. Our study further shows that adding a high-contrast checkerboard background can reduce such effect. In our third study, we discover that the resolution of a mesh used in the simulation of deformable objects can be reduced to a certain level without being noticed. For complex deformation, it is easier for people to recognize such reduction. Lastly, we verify two hypotheses which are assumed to be true only intuitively in many rigid body simulations in our third study. I: In large scale rigid body simulation, viewers may not be able to perceive distortion incurred by an approximated simulation method. II: Fixing objects under a pile of objects does not affect the visual plausibility. Our analysis of results supports the truthfulness of the hypotheses under certain simulation environments, but discovers four factors which may affect the authenticity of these hypotheses: number of collisions simulated simultaneously, homogeneity of colliding object pairs, distance from scene under simulation to camera position, and simulation method used