The project is divided into five main work packages with dedicated subgoals. The first deals with the visual estimation of the optical properties of materials like glossiness or translucency, which provide important first indications of an object’s properties even before we reach out and touch them. So far, we have measured and modelled how we use local colour gradients in images to infer several different physical properties of surfaces, including glossiness, 3D shape, and surface colour.
The second work package deals with how we infer the mechanical properties of materials, like how hard or soft they are, especially from the way they move or change shape over time. So far, we have developed models that can predict the perceptual properties of materials from images and movies.
The third work package investigates how the physical properties of objects and materials impact the way that we grasp and interact with them. Previous work on grasping has typically focussed on extremely limited objects like artificial cubes and cylinders, and has often restricted grasping to the so-called “precision grip”—the thumb-and-index finger technique that humans use when holding small items, like a needle. In this work package, we have developed novel methods to measure exactly how we grasp everyday objects using our whole hands, and we are currently in the process of collecting a large dataset of such interactions.
The fourth work package investigates how sculptors make one material (e.g. marble) look like other materials (e.g. skin, cloth). This can give us unique insights into the 3D cues that the brain uses to work out physical properties. So far, we have performed studies showing how even tiny local features, like a little ripple can allow the brain to infer the presence of cloth over extended surface regions, implying the brain has sophisticated mechanisms for interpolating local cues.
The last work package is trying to work out how we imagine, visualise and predict physical events as they unfold over time. Prompted by a momentary snapshot of a scene—like a precariously balanced stack of plates—we can usually play out a “mental movie” of what will happen next so we can work out which direction and speed we expect objects to move, whether they will break, how far they will scatter and so on. To investigate this, so far, we have developed a computational model that can predict how we mentally rotate objects in our “mind’s eye”.