Generative artificial intelligence (AI) models unlock new ways to create images, emerging as a new medium alongside paintings, photographs, physically based renderings (PBR), etc. Generative AI images can be perceptually convincing without being physically plausible, allowing to investigate the boundaries of visual perception. This study examines whether generative AI images adhere to a medium-independent perceptual space converged from previous studies. We compared the perceptual similarity of images from three generative AI models against a bidirectional reflectance distribution functions (BRDFs) PBR image dataset, using human similarity judgments. In experiment 1, we used the text descriptions of 32 materials (e.g., blue acrylic) from the Mitsubishi Electric Research Laboratories (MERL) BRDF dataset, prompting two text-to-image models, DALL-E 2 and Midjourney v2, to generate 32 sphere-shaped stimuli per model. Perceptual spaces derived from similarity judgments revealed that both AI models resulted in two-dimensional spaces whereas the MERL space was confined to one dimension, probably owing to a lack of surface texture. These unrelated perceptual spaces suggest the AI models generated unique and different images from identical text prompts. In experiment 2 we used the text-to-image model Stable Diffusion v1.5 with ControlNet for additional depth-map constraints. Using the same 32 descriptions, we generated 3 sets using 3 different depth maps. The three resulting perceptual spaces are all two-dimensional, exhibiting high similarity, indicating a robust and non-random structure. They also show a similar structure to the MERL space and perceptual spaces from other material studies using photographs, PBR, and depictions, suggesting AI-generated imagery may indeed be used as a new medium to explore material perception.

Purpose – This research examined how to present clothing fabrics online so that consumers gain an accurate impression. Providing online shoppers with accurate product information will lead to fewer product returns, offering clear economic and ecological benefits. Design/methodology/approach – Two studies (N = 90 and N = 379) assessed the accuracy of fabric perception in different online presentation conditions. A base condition showing conventional information was compared to three conditions with additional information: scrunched fabric pictures; a video of a model wearing the dress or a video showing hands interacting with the fabric. ANOVA tests assessed the effect of the online condition on fabric perception discrepancies between the online-presented and actual dress. Findings – A video in which hands interact with fabric, stretching, shaking, and crunching it, improved an accurate online fabric perception, specifically for stiffness and stretchability. A model video improved perception accuracy for glossiness. Scrunched fabric pictures improved accurate glossiness and thickness perception but worsened weight and stiffness perception for specific dresses. Practical implications – These findings aid companies in making an informed decision on how to present fabrics with certain properties online in order to reduce product returns. Originality/value – Existing research on the effect of different types of product presentation mainly focused on heightening purchase intention. We focused on increasing actual fabric perception accuracy, which will aid in adopting a more sustainable retail strategy by preventing unnecessary returns.

We present a framework that connects ideas from the visual arts and visual perception. It adapts two existing frameworks for the analysis of form and content so that it can be used in an educational context for teaching perception through visual arts. The basis is the formal analysis of texture, colour, light, space, and material. This analysis can be conducted both on the medium and the motif, which adds a second level in addition to the formal level. Thirdly, a conte(n/x)t level is discussed which combines a basic notion of semiotics and iconography. We share our experience of implementing pictorial analysis in design and perception education and discuss how the framework is used both in a quantitative and a qualitative fashion. Next to education, the framework provides a basis for further pictorial research.

The spectral shape, irradiance, direction, and diffuseness of daylight vary regularly throughout the day. The variations in illumination and their effect on the light reflected from objects may in turn provide visual information as to the time of day. We suggest that artists' color choices for paintings of outdoor scenes might convey this information and that therefore the time of day might be decoded from the colors of paintings. Here we investigate whether human viewers' estimates of the depicted time of day in paintings correlate with their image statistics, specifically chromaticity and luminance variations. We tested time-of-day perception in 17th- to 20th-century Western European paintings via two online rating experiments. In Experiment 1, viewers' ratings from seven time choices varied significantly and largely consistently across paintings but with some ambiguity between morning and evening depictions. Analysis of the relationship between image statistics and ratings revealed correlations with the perceived time of day: higher "morningness" ratings associated with higher brightness, contrast, and saturation and darker yellow/brighter blue hues; "eveningness" with lower brightness, contrast, and saturation and darker blue/brighter yellow hues. Multiple linear regressions of extracted principal components yielded a predictive model that explained 76% of the variance in time-of-day perception. In Experiment 2, viewers rated paintings as morning or evening only; rating distributions differed significantly across paintings, and image statistics predicted people's perceptions. These results suggest that artists used different color palettes and patterns to depict different times of day, and the human visual system holds consistent assumptions about the variation of natural light depicted in paintings.

We investigated the influence of the medium on the perception of depicted objects and materials. Oil paintings and their reproductions in engravings were chosen because they are vastly distinctive media while having completely identical content. A total of 15 pairs were collected, consisting of 88 fragments depicting different materials, including fabric, skin, wood and metal. Besides the original condition, we created three manipulations to understand the effect of colour (a greyscale version) and contrast (equalised histograms towards both painting and engraving). We performed rating experiments on five attributes: three-dimensionality, glossiness, convincingness, smoothness and softness. An average of 25 participants finished each of the 20 online experimental sessions (five attributes X four conditions). Besides clear correlations between the two media, the differences mainly show in their means (different levels of perceived attributes) and standard deviations (perceived range). In most sessions, paintings depict a wider range than engravings. In addition, it was the histogram equalisation (global contrast) that made the most impact on perceived attributes, rather than colour removal. This suggests that engravers compensated for the lack of colour by exploiting the possibilities of local contrast.

In European painting, a transition took place where artists started to consciously introduce blurred or soft contours in their works. There may have been several reasons for this. One suggestion in art historical literature is that this may have been done to create a stronger sense of volume in the depicted figures or objects. Here we describe four experiments in which we tried to test whether soft or blurred contours do indeed enhance a sense volume or depth. In the first three experiments, we found that, for both paintings and abstract shapes, three dimensionality was actually decreased instead of increased for blurred (and line) contours, in comparison with sharp contours. In the last experiment, we controlled for the position of the blur (on the lit or dark side) and found that blur on the lit side evoked a stronger impression of three dimensionality. Overall, the experiments robustly show that an art historical conjecture that a blurred contour increases three dimensionality is not granted. Because the blurred contours can be found in many established art works such as from Leonardo and Vermeer, there must be other rationales behind this use than the creation of a stronger sense of volume or depth.

Humans can rapidly identify materials, such as wood or leather, even within a complex visual scene. Given a single image, one can easily identify the underlying "stuff," even though a given material can have highly variable appearance; fabric comes in unlimited variations of shape, pattern, color, and smoothness, yet we have little trouble categorizing it as fabric. What visual cues do we use to determine material identity? Prior research suggests that simple "texture" features of an image, such as the power spectrum, capture information about material properties and identity. Few studies, however, have tested richer and biologically motivated models of texture. We compared baseline material classification performance to performance with synthetic textures generated from the Portilla-Simoncelli model and several common image degradations. The textures retain statistical information but are otherwise random. We found that performance with textures and most degradations was well below baseline, suggesting insufficient information to support foveal material perception. Interestingly, modern research suggests that peripheral vision might use a statistical, texture-like representation. In a second set of experiments, we found that peripheral performance is more closely predicted by texture and other image degradations. These findings delineate the nature of peripheral material classification.

Most studies on the perception of style have used whole scenes/entire paintings; in our study, we isolated a single motif (an apple) to reduce or even eliminate the influence of composition, iconography, and other contextual information. In this article, we empirically address two fundamental questions of the existence (Experiment 1) and description (Experiment 2) of style. We chose 48 cut-outs of mostly Western European paintings (15th to 21st century) that showed apples. In Experiment 1, 415 unique participants completed online triplet similarity tasks. Multidimensional scaling (MDS) reached a nonrandom three-dimensional (3D) embedding, showing that participants are able to judge stylistic differences in a systematic way. We also found a strong correlation between creation year and embedding, both a linear correlation with Dimension 2, and a rotational correlation in the first two dimensions. To interpret the embedding further, in Experiment 2, we fitted three color statistics and nine attribute ratings (glossiness, three-dimensionality, convincingness, brush coarseness, etc.) to the 3D perceptual style space. Results showed that Dimension 1 is associated with spatial attributes (Smoothness, Brushstroke coarseness) and Convincingness, Dimension 2 is related to Hue, and Dimension 3 is related to Chroma. The results suggest that texture and color are two important variables for style perception. By isolating the motifs, we could exclude higher levels of information such as composition and context. Interestingly, the results reinforce previous findings using whole scenes, suggesting that style can already be perceived in sometimes very small fragments of paintings.

We present a method to capture the 7-dimensional light field structure, and translate it into perceptually-relevant information. Our spectral cubic illumination method quantifies objective correlates of perceptually relevant diffuse and directed light components, including their variations over time, space, in color and direction, and the environment’s response to sky and sunlight. We applied it “in the wild”, capturing how light on a sunny day differs between light and shadow, and how light varies over sunny and cloudy days. We discuss the added value of our method for capturing nuanced lighting effects on scene and object appearance, such as chromatic gradients.

Visualizing biosignals can be important for social Virtual Reality (VR), where avatar non-verbal cues are missing. While several biosignal representations exist, designing effective visualizations and understanding user perceptions within social VR entertainment remains unclear. We adopt a mixed-methods approach to design biosignals for social VR entertainment. Using survey (N=54), context-mapping (N=6), and co-design (N=6) methods, we derive four visualizations. We then ran a within-subjects study (N=32) in a virtual jazz-bar to investigate how heart rate (HR) and breathing rate (BR) visualizations, and signal rate, influence perceived avatar arousal, user distraction, and preferences. Findings show that skeuomorphic visualizations for both biosignals allow differentiable arousal inference; skeuomorphic and particles were least distracting for HR, whereas all were similarly distracting for BR; biosignal perceptions often depend on avatar relations, entertainment type, and emotion inference of avatars versus spaces. We contribute HR and BR visualizations, and considerations for designing social VR entertainment biosignal visualizations.

In everyday scenes, the effective light (the actual light in a space) can be defined as a complex light field, resulting from a mixture of emissive light sources and indirect mutual surface (inter-)reflections. Hence, the light field typically consists of diffuse and directional illumination and varies in spectral irradiance as a function of location and direction. The spatially varying differences between the diffuse and directional illumination spectra induce correlated colour temperature (CCT) and colour rendition variations over the light fields. Here, we aim to investigate the colourimetric properties of the actual light, termed the effective CCT and colour rendition, for spaces of one reflectance (uni-chromatic spaces). The spectra of the diffuse light-field component (light density) and the directional light-field component (light vector) were measured in both physical and simulated uni-chromatic spaces illuminated by ordinary white light sources. We empirically tested the effective CCT and colour rendition for the light density and the light vector, separately. There were significant differences between the lamp-specified CCT and colour rendition and the actual light-based effective CCT and effective colour rendition. Inter-reflections predominantly affected the CCT and colour rendition of the light density relative to the light vector. Treating the diffuse and directional light-field components in a linear model reveals the separate influences of the light source and scene. These effects show the importance of a 3D version of colour checkers for lighting designers, architects or in general computer graphics applications, for which we propose simple Lambertian spheres.