image colon: Roland Fleming
Roland Fleming

Prof. Roland W. Fleming, PhD

Kurt Koffka Professor of Experimental Psychology

Giessen University
Department of Psychology
Otto-Behaghel-Str. 10
35394 Giessen (Germany)

Phone:
+49(0)641 / 99-26140
Fax:
+49(0)641 / 99-26119
roland.w.fleming@psychol.uni-giessen.de
Office:
Building F2, Room 351



Research Interests

Visual Perception of Objects and Materials

Research in my lab focusses on how we visually infer the physical properties of surfaces, materials and objects in our surroundings.

When we look at things, we don't experience the world as a meaningless jumble of lines, colours or motions. Instead, whenever we open our eyes, we immediately gain access to a richly detailed world of meaningful visual sensations. We recognise objects; perceive what things are made of; identify risk and pleasures and can even work out how objects might respond to forces or actions. Based on how things look, we are able to make a remarkable range of subtle judgements about the physical properties of objects, such as whether food is fresh or stale or whether an object is stable or likely to topple over. Without touching an object, we can usually work out what it would feel like were we to reach out and touch it, based on the curves and contours of its shape and the way light plays across its surface. My research program aims to understand how the brain estimates the 3D shape of surfaces, and the material properties of objects such as elasticity, translucency or viscosity. In order to do this, we use a combination of computer graphics, image analysis techniques, neural modelling and psychophysical experiments.


Short Bio

Roland Fleming

Roland Fleming is the Executive Director of the Center For Mind, Brain and Behaviour (CMMB) of the Universities of Marburg and Giessen, and co-Spokesperson for the Research Cluster The Adaptive Mind. He is an interdisciplinary researcher specializing in the visual perception of materials, illumination and 3D shape. He did his undergraduate degree in Psychology, Philosophy and Physiology at Oxford University, graduating with First Class Honours in 1999, and completed his PhD in the Department of Brain and Cognitive Sciences at MIT in 2004. He then served as a project leader at the Max Planck Institute for Biological Cybernetics in Tübingen. In 2010 he joined Giessen University as a junior professor. Since 2016, has been the Kurt Koffka Professor of Experimental Psychology.

His research combines psychophysics, neural modelling, computer graphics and image analysis to understand how the brain estimates the physical properties of objects. He has conducted a wide variety of studies on the perception of material properties such as glossiness, translucency, and viscosity and has applied insights from this work to the development of computer graphics algorithms for simulating material appearances. Roland Fleming has served as joint Editor-In-Chief of ACM Transactions on Applied Perception, an interdisciplinary journal dedicated to using perception to advance computer graphics and other fields. In 2012 he was awarded the Faculty Research Prize from the University of Giessen and in 2013 he was awarded the Young Investigator Award by the Vision Sciences Society. In 2016 was awarded an ERC Consolidator Grant for the project “SHAPE: On the perception of growth, form and process” and in 2023 was awarded an ERC Advanced Grant for the project “STUFF: Visual Perception of Materials and their Properties”. In 2022, he was elected Fellow of the Royal Society of Biology. For more information see the Wikipedia entry on Roland Fleming.


Keywords

psychology | cognitive science | computational neuroscience | sensory, perceptual and motor processes | human visual perception | mid-level vision | material perception | surface perception | object perception | optical properties of materials | mechanical properties of materials | binocular stereopsis | motion | colour | shading | texture | contours | perceptual organization | grouping | transparency | translucency | gloss | sub-surface scattering | shape-from-x | intrinsic image analysis | categorization | one-shot learning | generative models | perceptual organization | computer graphics | BRDF | photorealism | image-based editing | physics simulation | HDR images | tonemapping | perceptually-inspired computer graphics | motor-control | grasping | precision grip | multidigit grasping | psychophysics | behavioural experiments | motion tracking | computational modelling | machine learning | deep learning | deep neural networks | unsupervised learning | self-supervised learning.

Selected Publications

  • Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

    scene 2,3,7,8
    Authors:
    Hartmann, Maiello, Rothkopf & Fleming
    Journal:
    Jove
    Summary:
    We demonstrate a workflow for estimating full contact regions between the human hand and objects as participants grasp them naturally, by measuring intersections between a mesh reconstruction of the hand and a virtual mesh of the object.
    Citation:
    Hartmann, F., Maiello, G., Rothkopf, C. A., Fleming, R. W. Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping. J. Vis. Exp. (194), e64877, https://doi.org/10.3791/64877
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  • Mental object rotation based on two-dimensional visual representations

    scene 2,3,7,8
    Authors:
    Stewart, Hartmann, Morgenstern, Storrs, Maiello and Fleming
    Journal:
    Current Biology
    Summary:
    Mental comparisons of 3D object viewpoints have been assumed to occur on 3D representations. We show that non-uniformities in object viewpoint comparisons are captured by a 2D model, suggesting that such computations rely on a ‘mental rendering’ of the simulated proximal viewpoint transformation.
    Citation:
    Stewart, E.E.M., Hartmann, F.T., Morgenstern, Y., Storrs, K.R., Maiello, G., and Fleming, R.W. (in press). Mental object rotation based on two-dimensional visual representations, Current Biology.
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  • One shot generalization in humans revealed through a drawing task

    title=
    Authors:
    Tiedemann, Morgenstern, Schmidt & Fleming
    Journal:
    eLife
    Summary:
    We analyze how humans are able to create a whole category of objects inspired by justa single exemplar object. We found that observers alter the part-structure of the exemplar in systematic ways, with certain parts beeing deemed as more important than others, to create a novel member of that category.
    Citation:
    Tiedemann, H., Morgenstern, Y., Schmidt, F. & Fleming, R. W. (2022). One shot generalization in humans revealed through a drawing task. eLife, doi:https://doi.org/10.7554/eLife.75485
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  • Unsupervised learning predicts human perception and misperception of gloss.

    title=
    Authors:
    Storrs, Anderson & Fleming
    Journal:
    Nature Human Behavior
    Summary:
    We find that unsupervised neural networks trained to compress and predict images of surfaces spontaneously learn to organize images by their gloss, lighting and other world factors. The networks correctly predict both successes and striking failures of human gloss constancy, where other models fail.
    Citation:
    Storrs, KR, Anderson, BL & RW Fleming (2021). Unsupervised learning predicts human perception and misperception of gloss. Nature Human Behavior https://doi.org/10.1038/s41562-021-01097-6
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  • The Veiled Virgin illustrates visual segmentation of shape by cause.

    Statue of the Veiled Virgin
    Authors:
    Phillips & Fleming
    Journal:
    Proceedings of the National Academy of Sciences
    Summary:
    Inspired by a famous statue depicting the Virgin Mary wearing a transparent veil, we investigated how observers visually segment 3D shapes into multiple 'causal layers'.
    Citation:
    Phillips F* & RW Fleming* (2020). The Veiled Virgin illustrates visual segmentation of shape by cause. Proceedings of the National Academy of Sciences 201917565; DOI: 10.1073/pnas.1917565117
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  • Learning to See Stuff

    Textiles, leather, mashed potatoes
    Authors:
    Fleming & Storrs
    Journal:
    Current Opinion in Behavioral Sciences
    Summary:
    An introduction to unsupervised deep learning approaches to vision, which advocates for such 'statistical appearance models' as being more appropriate ways to think about material perception than classic 'inverse optics' frameworks.
    Citation:
    Fleming RW & KR Storrs (2019) Learning to See Stuff. Current Opinion in Behavioral Sciences. 30: 100–108. https://doi.org/10.1016/j.cobeha.2019.07.004

  • Visual Features in the Perception of Liquids

    A green gel poured on a surface
    Authors:
    Van Assen, Barla, Fleming
    Journal:
    Current Biology
    Summary:
    Here, using state-of-the-art fluid simulations, we investigate the cues underlying the perception of liquid viscosity. We suggest that midlevel visual features describing a liquids shape and motion play a key role in viscosity perception Citation:
    Citation:
    Van Assen, JJ, Barla P & Fleming, RW (2018). Visual Features in the Perception of Liquids. Current Biology 28(3), 452–458. https://doi.org/10.1016/j.cub.2017.12.037
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  • Material Perception

    materials
    Authors:
    Fleming
    Journal:
    Annual Reviews of Vision Science
    Summary:
    This paper reviews recent work on material perception and scrutinizes some basic assumptions of mid-level vision to propose an alternative approach to how material properties might be estimated and represented.
    Citation:
    Fleming RW (2017). Material Perception. Annual Reviews of Vision Science 3(1). 365-388. doi: 10.1146/annurev-vision-102016-061429

  • Flow-Guided Warping for Image-Based Shape Manipulation

    lion
    Authors:
    Vergne, Barla, Bonneau, Fleming
    Journal:
    ACM Transactions on Graphics
    Summary:
    We present an interactive method that manipulates perceived object shape using a spatial warping technique to exaggerate orientation patterns in the image that are strongly correlated to surface curvature. Our algorithm produces convincing shape manipulation results on synthetic images and photographs, for various materials and lighting environments.
    Citation:
    Vergne, R., Barla P., Bonneau, G.-P. and R. W. Fleming (2016). Flow-Guided Warping for Image-Based Shape Manipulation. ACM Transactions on Graphics (Proceedings of SIGGRAPH 2016), 35(4): 93. DOI: http://dx.doi.org/10.1145/2897824.2925937.

  • 'Proto-rivalry': how the binocular brain identifies gloss

    Reflecting Blob
    Authors:
    Muryy, Fleming, Welchman
    Journal:
    Proceedings of the Royal Society of London
    Summary:
    Here, we show that the brain exploits some surprisingly simple binocular fusion signals to identify glossy surfaces. We show that the widely held concept of 'binocular luster' is wrong. Instead it is the partial fusion that occurs when binocular matches stray from epipolar geometry that drives binocular gloss perception.
    Citation:
    Muryy, A.A., R.W. Fleming and A.E. Welchman (2016). 'Proto-rivalry': how the binocular brain identifies gloss. Proceedings of the Royal Society of London (B), 283: 20160383. doi: 10.1098/rspb.2016.0383

  • Visual Perception of Materials and their Properties

    Mirror reflections
    Authors:
    Fleming
    Journal:
    Vision Research
    Summary:
    Here we review the perception of materials and propose that the visual system uses generative statisical models of material appearance to represent and distinguish between different materials.
    Citation:
    Fleming, R.W. (2014). Visual Perception of Materials and their Properties. Vision Research, 94, 62-75 https://doi.org/10.1016/j.visres.2013.11.004

  • Specular reflections and the estimation of shape from binocular disparity

    reflecting sphere
    Authors:
    Muryy, Welchman, Blake, Fleming
    Journal:
    Proceedings of the National Academy of Sciences
    Summary:
    We studied binocular 3D shape perception for ideal mirrored surfaces, finding that far from 'knowing the physics' of specular reflection, they make substantial errors. The responses are consistent with a process that identifies reliable local disparity signals and interpolates between them.
    Citation:
    Muryy, A., Welchman, A.E., Blake, A. and R.W. Fleming (2013). Specular reflections and the estimation of shape from binocular disparity. Proceedings of the National Academy of Sciences, 110(6): 2413-2418. https://doi.org/10.1073/pnas.1212417110

  • Estimation of 3D shape from image orientations

    shape from smear
    Authors:
    Fleming, Holtmann-Rice, Bülthoff
    Journal:
    Proceedings of the National Academy of Sciences
    Summary:
    Here we show that the perception of 3D shape from surface texture patterns is based primarily on specific 2D orientation signals. Perceptually adapting orientation detectors causes unoriented random noise to look like specific 3D shapes, leading to striking illusions.
    Citation:
    Fleming, R. W., Holtmann-Rice, D. and H. H. Bülthoff (2011). Estimation of 3D shape from image orientations. Proceedings of the National Academy of Sciences, 108(51) 20438-20443. https://doi.org/10.1073/pnas.1114619109

  • Visual Motion and the Perception of Surface Material

    painting reflections
    Authors:
    Doerschner, Fleming, Yilmaz, Schrater, Hartung, Kersten
    Journal:
    Current Biology
    Summary:
    In this paper we show the central role that optical flow plays in the perception of surface glossiness. We show that sticking reflections onto a surface makes it look matte, and identify specific optic flow characteristics that can predict surface material illusions.
    Citation:
    Doerschner, K. Fleming, R.W., Yilmaz, O., Schrater, P.R., Hartung, B. and Kersten, D. (2011). Visual Motion and the Perception of Surface Material. Current Biology, 21(23): 1-7. https://doi.org/10.1016/j.cub.2011.10.036
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