Discerning nonrigid 3D shapes from motion cues

A role of rectangularity in perceiving a 3D shape of an object

Rectangularity and perpendicularity of contours are important properties of 3D shape for the visual system and the visual system can use them asa prioriconstraints for perceivingshape veridically. The presentarticle provides a comprehensive review ofpriorstudiesofthe perception of rectangularity and perpendicularity anditdiscussestheir effects on3D shape perception from both theoretical and empiricalapproaches. It has been shown that the visual system is biased to perceive a rectangular 3D shape from a 2D image. We thought that this bias might be attributable to the likelihood of a rectangular interpretation but this hypothesis is not supported by the results of our psychophysical experiment. Note that the perception ofa rectangular shape cannot be explained solely on the basis of geometry. A rectangular shape is perceived from an image that is inconsistent with a rectangular interpretation. To address thisissue, we developed a computational model that can recover a rectangular shape from an image of a parallelopiped. The model allows the recovered shape to be slightly inconsistent so that the recovered shape satisfies the a priori constraints of maximum compactness and minimal surface area. This model captures someof thephenomenaassociated withthe perception of the rectangular shape that were reported inpriorstudies. This finding suggests that rectangularity works for shape perception by incorporatingitwith someadditionalconstraints.

Perceptual Biases in the Interpretation of Non-Rigid Shape Transformations from Motion

Most existing research on the perception of 3D shape from motion has focused on rigidly moving objects. However, many natural objects deform non-rigidly, leading to image motion with no rigid interpretation. We investigated potential biases underlying the perception of non-rigid shape interpretations from motion. We presented observers with stimuli that were consistent with two qualitatively different interpretations. Observers were shown a two-part 3D object with the smaller part changing in length dynamically as the whole object rotated back and forth. In two experiments, we studied the misperception (i.e., perceptual reinterpretation) of the non-rigid length change to a part. In Experiment 1, observers misperceived this length change as a part orientation change (i.e., the smaller part was seen as articulating with respect to the larger part). In Experiment 2, the stimuli were similar, except the silhouette of the part was visible in the image. Here, the non-rigid length change was reinterpreted as a rigidly attached part with an "illusory" non-orthogonal horizontal angle relative to the larger part. We developed a model that incorporated this perceptual reinterpretation and could predict observer data. We propose that the visual system may be biased towards part-wise rigid interpretations of non-rigid motion, likely due to the ecological significance of movements of humans and other animals, which are generally constrained to move approximately part-wise rigidly. That is, not all non-rigid deformations are created equal: the visual systems' prior expectations may bias the system to interpret motion in terms of biologically plausible shape transformations.

Different extrapolation of moving object locations in perception, smooth pursuit, and saccades

The ability to accurately perceive and track moving objects is crucial for many everyday activities. In this study, we use a "double-drift stimulus" to explore the processing of visual motion signals that underlie perception, pursuit, and saccade responses to a moving object. Participants were presented with peripheral moving apertures filled with noise that either drifted orthogonally to the aperture's direction or had no net motion. Participants were asked to saccade to and track these targets with their gaze as soon as they appeared and then to report their direction. In the trials with internal motion, the target disappeared at saccade onset so that the first 100 ms of the postsaccadic pursuit response was driven uniquely by peripheral information gathered before saccade onset. This provided independent measures of perceptual, pursuit, and saccadic responses to the double-drift stimulus on a trial-by-trial basis. Our analysis revealed systematic differences between saccadic responses, on one hand, and perceptual and pursuit responses, on the other. These differences are unlikely to be caused by differences in the processing of motion signals because both saccades and pursuits seem to rely on shared target position and velocity information. We conclude that our results are instead due to a difference in how the processing mechanisms underlying perception, pursuit, and saccades combine motor signals with target position. These findings advance our understanding of the mechanisms underlying dissociation in visual processing between perception and eye movements.

Perceptual transitions between object rigidity and non-rigidity: Competition and cooperation among motion energy, feature tracking, and shape-based priors

Why do moving objects appear rigid when projected retinal images are deformed non-rigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly, but rigid rotation was reported at slow speeds. When gaps, paint, or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation gave a high probability of wobbling for the motion-energy flows. However, the convolutional neural network gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining convolutional neural network outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow speeds, with the shape-based priors for wobbling and rolling, explained rigid and non-rigid percepts across shapes and speeds (R2 = 0.95). The results demonstrate how cooperation and competition between different neuronal classes lead to specific states of visual perception and to transitions between the states.

Perceptual Transitions between Object Rigidity & Non-rigidity: Competition and cooperation between motion-energy, feature-tracking and shape-based priors

Why do moving objects appear rigid when projected retinal images are deformed non-rigidly? We used rotating rigid objects that can appear rigid or non-rigid to test whether shape features contribute to rigidity perception. When two circular rings were rigidly linked at an angle and jointly rotated at moderate speeds, observers reported that the rings wobbled and were not linked rigidly but rigid rotation was reported at slow speeds. When gaps, paint or vertices were added, the rings appeared rigidly rotating even at moderate speeds. At high speeds, all configurations appeared non-rigid. Salient features thus contribute to rigidity at slow and moderate speeds, but not at high speeds. Simulated responses of arrays of motion-energy cells showed that motion flow vectors are predominantly orthogonal to the contours of the rings, not parallel to the rotation direction. A convolutional neural network trained to distinguish flow patterns for wobbling versus rotation, gave a high probability of wobbling for the motion-energy flows. However, the CNN gave high probabilities of rotation for motion flows generated by tracking features with arrays of MT pattern-motion cells and corner detectors. In addition, circular rings can appear to spin and roll despite the absence of any sensory evidence, and this illusion is prevented by vertices, gaps, and painted segments, showing the effects of rotational symmetry and shape. Combining CNN outputs that give greater weight to motion energy at fast speeds and to feature tracking at slow, with the shape-based priors for wobbling and rolling, explained rigid and nonrigid percepts across shapes and speeds (R2=0.95). The results demonstrate how cooperation and competition between different neuronal classes leads to specific states of visual perception and to transitions between the states.

Perceptual judgments for the softness of materials under indentation

Humans can judge the softness of elastic materials through only visual cues. However, factors contributing to the judgment of visual softness are not yet fully understood. We conducted a psychophysical experiment to determine which factors and motion features contribute to the apparent softness of materials. Observers watched video clips in which materials were indented from the top surface to a certain depth, and reported the apparent softness of the materials. The depth and speed of indentation were systematically manipulated. As physical characteristics of materials, compliance was also controlled. It was found that higher indentation speeds resulted in larger softness rating scores and the variation with the indentation speed was successfully explained by the image motion speed. The indentation depth had a powerful effect on the softness rating scores and the variation with the indentation depth was consistently explained by motion features related to overall deformation. Higher material compliance resulted in higher softness rating scores and these variation with the material compliance can be explained also by overall deformation. We conclude that the brain makes visual judgments about the softness of materials under indentation on the basis of the motion speed and deformation magnitude.

The Z-Box illusion: dominance of motion perception among multiple 3D objects

In the present article, we examine a novel illusion of motion-the Z-Box illusion-in which the presence of a bounding object influences the perception of motion of an ambiguous stimulus that appears within. Specifically, the stimuli are a structure-from-motion (SFM) particle orb and a wireframe cube. The orb could be perceived as rotating clockwise or counterclockwise while the cube could only be perceived as moving in one direction. Both stimuli were presented on a two-dimensional (2D) display with inferred three-dimensional (3D) properties. In a single experiment, we examine motion perception of a particle orb, both in isolation and when it appears within a rotating cube. Participants indicated the orb's direction of motion and whether the direction changed at any point during the trial. Accuracy was the critical measure while motion direction, the number of particles in the orb and presence of the wireframe cube were all manipulated. The results suggest that participants could perceive the orb's true rotation in the absence of the cube so long as it was made up of at least ten particles. The presence of the cube dominated perception as participants consistently perceived congruent motion of the orb and cube, even when they moved in objectively different directions. These findings are considered as they relate to prior research on motion perception, computational modelling of motion perception, structure from motion and 3D object perception.

Perceptual Properties of the Poisson Effect

When an elastic material (e.g., fabric) is horizontally stretched (or compressed), the material is compressed (or extended) vertically – so-called the Poisson effect. In the different case of the Poisson effect, when an elastic material (e.g., rubber) is vertically squashed, the material is horizontally extended. In both cases, the visual system receives image deformations involving horizontal expansion and vertical compression. How does the brain disentangle the two cases and accurately distinguish stretching from squashing events? Manipulating the relative magnitude of the deformation of a square between horizontal and vertical dimensions in the two-dimensional stimuli, we asked observers to judge the force direction in the stimuli. Specifically, the participants reported whether the square was stretched or squashed. In general, the participant’s judgment was dependent on the relative deformation magnitude. We also checked the anisotropic effect of deformation direction [i.e., horizontal vs. vertical stretching (or squashing)] and found that the participant’s judgment was strongly biased toward horizontal stretching. We also observed that the asymmetric deformation pattern, which indicated the specific context of force direction, was also a strong cue to the force direction judgment. We suggest that the brain judges the force direction in the Poisson effect on the basis of assumptions about the relationship between image deformation and force direction, in addition to the relative image deformation magnitudes between horizontal and vertical dimensions.

Expectations affect the perception of material properties

Many objects that we encounter have typical material qualities: spoons are hard, pillows are soft, and Jell-O dessert is wobbly. Over a lifetime of experiences, strong associations between an object and its typical material properties may be formed, and these associations not only include how glossy, rough, or pink an object is, but also how it behaves under force: we expect knocked over vases to shatter, popped bike tires to deflate, and gooey grilled cheese to hang between two slices of bread when pulled apart. Here we ask how such rich visual priors affect the visual perception of material qualities and present a particularly striking example of expectation violation. In a cue conflict design, we pair computer-rendered familiar objects with surprising material behaviors (a linen curtain shattering, a porcelain teacup wrinkling, etc.) and find that material qualities are not solely estimated from the object's kinematics (i.e., its physical [atypical] motion while shattering, wrinkling, wobbling etc.); rather, material appearance is sometimes “pulled” toward the “native” motion, shape, and optical properties that are associated with this object. Our results, in addition to patterns we find in response time data, suggest that visual priors about materials can set up high-level expectations about complex future states of an object and show how these priors modulate material appearance.

Mental geometry of three-dimensional size perception

Judging the poses, sizes, and shapes of objects accurately is necessary for organisms and machines to operate successfully in the world. Retinal images of three-dimensional objects are mapped by the rules of projective geometry and preserve the invariants of that geometry. Since Plato, it has been debated whether geometry is innate to the human brain, and Poincare and Einstein thought it worth examining whether formal geometry arises from experience with the world. We examine if humans have learned to exploit projective geometry to estimate sizes and aspects of three-dimensional shape that are related to relative lengths and aspect ratios.

Numerous studies have examined size invariance as a function of physical distance, which changes scale on the retina. However, it is surprising that possible constancy or inconstancy of relative size seems not to have been investigated for object pose, which changes retinal image size differently along different axes. We show systematic underestimation of length for extents pointing toward or away from the observer, both for static objects and dynamically rotating objects. Observers do correct for projected shortening according to the optimal back-transform, obtained by inverting the projection function, but the correction is inadequate by a multiplicative factor. The clue is provided by the greater underestimation for longer objects, and the observation that they seem to be more slanted toward the observer. Adding a multiplicative factor for perceived slant in the back-transform model provides good fits to the corrections used by observers. We quantify the slant illusion with two different slant matching measurements, and use a dynamic demonstration to show that the slant illusion perceptually dominates length nonrigidity.

In biological and mechanical objects, distortions of shape are manifold, and changes in aspect ratio and relative limb sizes are functionally important. Our model shows that observers try to retain invariance of these aspects of shape to three-dimensional rotation by correcting retinal image distortions due to perspective projection, but the corrections can fall short. We discuss how these results imply that humans have internalized particular aspects of projective geometry through evolution or learning, and if humans assume that images are preserving the continuity, collinearity, and convergence invariances of projective geometry, that would simply explain why illusions such as Ames’ chair appear cohesive despite being a projection of disjointed elements, and thus supplement the generic viewpoint assumption.

A Computational Mechanism for Seeing Dynamic Deformation

Human observers perceptually discriminate the dynamic deformation of materials in the real world. However, the psychophysical and neural mechanisms responsible for the perception of dynamic deformation have not been fully elucidated. By using a deforming bar as the stimulus, we showed that the spatial frequency of deformation was a critical determinant of deformation perception. Simulating the response of direction-selective units (i.e., MT pattern motion cells) to stimuli, we found that the perception of dynamic deformation was well explained by assuming a higher-order mechanism monitoring the spatial pattern of direction responses. Our model with the higher-order mechanism also successfully explained the appearance of a visual illusion wherein a static bar apparently deforms against a tilted drifting grating. In particular, it was the lower spatial frequencies in this pattern that strongly contributed to the deformation perception. Finally, by manipulating the luminance of the static bar, we observed that the mechanism for the illusory deformation was more sensitive to luminance than contrast cues.

Seeing through transparent layers

The human visual system is remarkably good at decomposing local and global deformations in the flow of visual information into different perceptual layers, a critical ability for daily tasks such as driving through rain or fog or catching that evasive trout. In these scenarios, changes in the visual information might be due to a deforming object or deformations due to a transparent medium, such as structured glass or water, or a combination of these. How does the visual system use image deformations to make sense of layering due to transparent materials? We used eidolons to investigate equivalence classes for perceptually similar transparent layers. We created a stimulus space for perceptual equivalents of a fiducial scene by systematically varying the local disarray parameters reach and grain. This disarray in eidolon space leads to distinct impressions of transparency, specifically, high reach and grain values vividly resemble water whereas smaller grain values appear diffuse like structured glass. We asked observers to adjust image deformations so that the objects in the scene looked like they were seen (a) under water, (b) behind haze, or (c) behind structured glass. Observers adjusted image deformation parameters by moving the mouse horizontally (grain) and vertically (reach). For two conditions, water and glass, we observed high intraobserver consistency: responses were not random. Responses yielded a concentrated equivalence class for water and structured glass.

The motion-induced contour revisited: Observations on 3-D structure and illusory contour formation in moving stimuli

The motion-induced contour (MIC) was first described by Victor Klymenko and Naomi Weisstein in a series of papers in the 1980s. The effect is created by rotating the outline of a tilted cube in depth. When one of the vertical edges is removed, an illusory contour can be seen in its place. In four experiments, we explored which stimulus features influence perceived illusory contour strength. Participants provided subjective ratings of illusory contour strength as a function of orientation of the stimulus, separation between inducing edges, and the length of inducing edges. We found that the angle of tilt of the object in depth had the largest impact on perceived illusory contour strength with tilt angles of 20° and 30° producing the strongest percepts. Tilt angle is an unexplored feature of structure-from-motion displays. In addition, we found that once the depth structure of the object was extracted, other features of the display, such as the distance spanned by the illusory contour, could also influence its strength, similar to the notion of support ratio for 2-D illusory contours. Illusory contour strength was better predicted by the length of the contour in 3-D rather than in 2-D, suggesting that MICs are constructed by a 3-D process that takes as input initially recovered contour orientation and position information in depth and only then forms interpolations between them.

Dynamic Visual Cues for Differentiating Mirror and Glass

Mirror materials (perfect specular surfaces such as polished metal) and glass materials (transparent and refraction media) are quite commonly encountered in everyday life. The human visual system can discriminate these complex distorted images formed by reflection or transmission of the surrounding environment even though they do not intrinsically possess surface colour. In this study, we determined the cues that aid mirror and glass discrimination. From video analysis, we found that glass objects have more opposite motion components relative to the direction of object rotation. Then, we hypothesised a model developed using motion transparency because motion information is not only present on the front side, but also on the rear side of the object surface in the glass material object. In materials judging experiments, we found that human performance with rotating video stimuli is higher than that with static stimuli (simple images). Subsequently, we compared the developed model derived from motion coherency to human rating performance for transparency and specular reflection. The model sufficiently identified the different materials using dynamic information. These results suggest that the visual system relies on dynamic cues that indicate the difference between mirror and glass.

Dynamic Volume Completion and Deformation

A new class of dynamic volume completion is introduced, where image elements (e.g., occluding semi-ellipses placed at the edge of an object) can link across a gap between two or more objects, leading to the perception of illusory volumes that deform as those image elements are set into relative motion. These new demonstrations provide further evidence that volume completion is not dictated solely by contour relatability constraints, but is instead a dynamic process of 3D shape construction that also takes into account dynamic cues to object shape, even in the absence of any contour relatability whatsoever.

What Property of the Contour of a Deforming Region Biases Percepts toward Liquid?

Human observers can perceive the existence of a transparent surface from dynamic image deformation. They can also easily discriminate a transparent solid material such as plastic and glass from a transparent fluid one such as water and shampoo just by viewing them. However, the image information required for material discrimination of this sort is still unclear. A liquid changes its contour shape non-rigidly. We therefore examined whether additional properties of the contour of a deformation-defined region, which indicated contour non-rigidity, biased percepts of the region toward liquid materials. Our stimuli had a translating circular region wherein a natural texture image was deformed at the spatiotemporal deformation frequency that was optimal for the perception of a transparent layer. In Experiment 1, we dynamically deformed the contour of the circular region and found that large deformation of the contour biased the percept toward liquid. In Experiment 2, we manipulated the blurriness of the contour and observed that a strongly blurred contour biased percepts toward liquid. Taken together, the results suggest that a deforming region lacking a discrete contour biases percepts toward liquid.

Perceptual transparency from image deformation

Human vision has a remarkable ability to perceive two layers at the same retinal locations, a transparent layer in front of a background surface. Critical image cues to perceptual transparency, studied extensively in the past, are changes in luminance or color that could be caused by light absorptions and reflections by the front layer, but such image changes may not be clearly visible when the front layer consists of a pure transparent material such as water. Our daily experiences with transparent materials of this kind suggest that an alternative potential cue of visual transparency is image deformations of a background pattern caused by light refraction. Although previous studies have indicated that these image deformations, at least static ones, play little role in perceptual transparency, here we show that dynamic image deformations of the background pattern, which could be produced by light refraction on a moving liquid's surface, can produce a vivid impression of a transparent liquid layer without the aid of any other visual cues as to the presence of a transparent layer. Furthermore, a transparent liquid layer perceptually emerges even from a randomly generated dynamic image deformation as long as it is similar to real liquid deformations in its spatiotemporal frequency profile. Our findings indicate that the brain can perceptually infer the presence of "invisible" transparent liquids by analyzing the spatiotemporal structure of dynamic image deformation, for which it uses a relatively simple computation that does not require high-level knowledge about the detailed physics of liquid deformation.

Perception of shape and space across rigid transformations

Objects in our environment are subject to manifold transformations, either of the physical objects themselves or of the object images on the retina. Despite drastic effects on the objects' physical appearances, we are often able to identify stable objects across transformations and have strong subjective impressions of the transformations themselves. This suggests the brain is equipped with sophisticated mechanisms for inferring both object constancy, and objects' causal history. We employed a dot-matching task to study in geometrical detail the effects of rigid transformations on representations of shape and space. We presented an untransformed 'base shape' on the left side of the screen and its transformed counterpart on the right (rotated, scaled, or both). On each trial, a dot was superimposed at a given location on the contour (Experiment 1) or within and around the shape (Experiment 2). The participant's task was to place a dot at the corresponding location on the right side of the screen. By analyzing correspondence between responses and physical transformations, we tested for object constancy, causal history, and transformation of space. We find that shape representations are remarkably robust against rotation and scaling. Performance is modulated by the type and amount of transformation, as well as by contour saliency. We also find that the representation of space within and around a shape is transformed in line with the shape transformation, as if shape features establish an object-centered reference frame. These findings suggest robust mechanisms for the inference of shape, space and correspondence across transformations.

Non-rigid illusory contours and global shape transformations defined by spatiotemporal boundary formation

Spatiotemporal boundary formation (SBF) is the perception of form, global motion, and continuous boundaries from relations of discrete changes in local texture elements (Shipley and Kellman, 1994). In two experiments, small, circular elements underwent small displacements whenever an edge of an invisible (virtual) object passed over them. Unlike previous studies that examined only rigidly translating objects, we tested virtual objects whose properties changed continuously. Experiment 1 tested rigid objects that changed in orientation, scale, and velocity. Experiment 2 tested objects that transformed non-rigidly taking on a series of shapes. Robust SBF occurred for all of the rigid transformations tested, as well as for non-rigid virtual objects, producing the perception of continuously bounded, smoothly deforming shapes. These novel illusions involve perhaps the most extreme cases of visual perception of continuous boundaries and shape from minimal information. They show that SBF encompasses a wider range of illusory phenomena than previously understood, and they present substantial challenges for existing models of SBF.

Perceptual learning: toward a comprehensive theory

Visual perceptual learning (VPL) is long-term performance increase resulting from visual perceptual experience. Task-relevant VPL of a feature results from training of a task on the feature relevant to the task. Task-irrelevant VPL arises as a result of exposure to the feature irrelevant to the trained task. At least two serious problems exist. First, there is the controversy over which stage of information processing is changed in association with task-relevant VPL. Second, no model has ever explained both task-relevant and task-irrelevant VPL. Here we propose a dual plasticity model in which feature-based plasticity is a change in a representation of the learned feature, and task-based plasticity is a change in processing of the trained task. Although the two types of plasticity underlie task-relevant VPL, only feature-based plasticity underlies task-irrelevant VPL. This model provides a new comprehensive framework in which apparently contradictory results could be explained.

Efficiency of extracting stereo-driven object motions

Most living things and many nonliving things deform as they move, requiring observers to separate object motions from object deformations. When the object is partially occluded, the task becomes more difficult because it is not possible to use two-dimensional (2-D) contour correlations (Cohen, Jain, & Zaidi, 2010). That leaves dynamic depth matching across the unoccluded views as the main possibility. We examined the role of stereo cues in extracting motion of partially occluded and deforming three-dimensional (3-D) objects, simulated by disk-shaped random-dot stereograms set at randomly assigned depths and placed uniformly around a circle. The stereo-disparities of the disks were temporally oscillated to simulate clockwise or counterclockwise rotation of the global shape. To dynamically deform the global shape, random disparity perturbation was added to each disk's depth on each stimulus frame. At low perturbation, observers reported rotation directions consistent with the global shape, even against local motion cues, but performance deteriorated at high perturbation. Using 3-D global shape correlations, we formulated an optimal Bayesian discriminator for rotation direction. Based on rotation discrimination thresholds, human observers were 75% as efficient as the optimal model, demonstrating that global shapes derived from stereo cues facilitate inferences of object motions. To complement reports of stereo and motion integration in extrastriate cortex, our results suggest the possibilities that disparity selectivity and feature tracking are linked, or that global motion selective neurons can be driven purely from disparity cues.

Perceived surface slant is systematically biased in the actively-generated optic flow

Humans make systematic errors in the 3D interpretation of the optic flow in both passive and active vision. These systematic distortions can be predicted by a biologically-inspired model which disregards self-motion information resulting from head movements (Caudek, Fantoni, & Domini 2011). Here, we tested two predictions of this model: (1) A plane that is stationary in an earth-fixed reference frame will be perceived as changing its slant if the movement of the observer's head causes a variation of the optic flow; (2) a surface that rotates in an earth-fixed reference frame will be perceived to be stationary, if the surface rotation is appropriately yoked to the head movement so as to generate a variation of the surface slant but not of the optic flow. Both predictions were corroborated by two experiments in which observers judged the perceived slant of a random-dot planar surface during egomotion. We found qualitatively similar biases for monocular and binocular viewing of the simulated surfaces, although, in principle, the simultaneous presence of disparity and motion cues allows for a veridical recovery of surface slant.

Visual motion and the perception of surface material

Many critical perceptual judgments, from telling whether fruit is ripe to determining whether the ground is slippery, involve estimating the material properties of surfaces. Very little is known about how the brain recognizes materials, even though the problem is likely as important for survival as navigating or recognizing objects. Though previous research has focused nearly exclusively on the properties of static images, recent evidence suggests that motion may affect the appearance of surface material. However, what kind of information motion conveys and how this information may be used by the brain is still unknown. Here, we identify three motion cues that the brain could rely on to distinguish between matte and shiny surfaces. We show that these motion measurements can override static cues, leading to dramatic changes in perceived material depending on the image motion characteristics. A classifier algorithm based on these cues correctly predicts both successes and some striking failures of human material perception. Together these results reveal a previously unknown use for optic flow in the perception of surface material properties.