Kenneth Vanhoey

Unity3d · Unity Labs

We show that Catmull‐Clark subdivision induces an invariant one‐to‐four refinement rule for halfedges that reduces to simple algebraic expressions. This has two important consequences. First, it allows to refine the halfedges of the input mesh, which completely describe its topology, concurrently in breadth‐first order. Second, it makes the computa...

Neural texture synthesis and style transfer are both powered by the Gram matrix as a means to measure deep feature statistics. Despite its ubiquity, this second-order feature descriptor has several shortcomings resulting in visual artifacts, ill-defined interpolation, or inability to capture spatial constraints. Many previous works acknowledge thes...

Machine learning based Single Image Intrinsic Decomposition (SIID) methods decompose a captured scene into its albedo and shading images by using the knowledge of a large set of known and realistic ground truth decompositions. Collecting and annotating such a dataset is an approach that cannot scale to sufficient variety and realism. We free oursel...

We harness modern intrinsic decomposition tools based on deep learning to increase their applicability on realworld use cases. Traditional techniques are derived from the Retinex theory: handmade prior assumptions constrain an optimization to yield a unique solution that is qualitatively satisfying on a limited set of examples. Modern techniques ba...

Geometric modifications of three-dimensional (3D) digital models are commonplace for the purpose of efficient rendering or compact storage. Modifications imply visual distortions that are hard to measure numerically. They depend not only on the model itself but also on how the model is visualized. We hypothesize that the model’s light environment a...

Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that translates photos taken by cameras with limited capabilities into DSLR-quality photos automatically. We tackle this problem by introducing a weakly supervised photo enhance...

VarCity - the Video is a short documentary-style CGI movie explaining the main outcomes of the 5-year Computer Vision research project VarCity. Besides a coarse overview of the research, we present the challenges that were faced in its production, induced by two factors: i) usage of imperfect research data produced by automatic algorithms, and ii)...

Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSL...

Navigation and interpretation of ultrasound (US) images require substantial expertise, the training of which can be aided by virtual-reality simulators. However, a major challenge in creating plausible simulated US images is the generation of realistic ultrasound speckle. Since typical ultrasound speckle exhibits many properties of Markov Random F...

We present a new deep supervised learning method for intrinsic decomposition of a single image into its albedo and shading components. Our contributions are based on a new fully convolutional neural network that estimates absolute albedo and shading jointly. As opposed to classical intrinsic image decomposition work, it is fully data-driven, hence...

View-dependent surface color of virtual objects can be represented by outgoing radiance of the surface. In this paper we tackle the processing of outgoing radiance stored as a vertex attribute of triangle meshes. Data resulting from an acquisition process can be very large and computationally intensive to render. We show that when reducing the glob...

Recent color transfer methods use local information to learn the transformation from a source to an exemplar image, and then transfer this appearance change to a target image. These solutions achieve very successful results for general mood changes, e.g., changing the appearance of an image from ``sunny'' to ``overcast''. However, such methods have...

Local random-phase noise is an efficient noise model for procedural texturing. It is defined on a regular spatial grid by local noises, which are sums of cosines with random phase. Our model is versatile thanks to separate samplings in the spatial and spectral domains. Therefore, it encompasses Gabor noise and noise by Fourier series. A stratified...

Vision and computer graphics communities have built methods for digitizing, processing and rendering 3D objects. There is an increasing demand coming from cultural communities for these technologies, especially for archiving, remote studying and restoring cultural artefacts like statues, buildings or caves. Besides digitizing geometry, there can be...

In computer graphics, rendering visually detailed scenes is often achieved through texturing. We propose a method for on-the-fly non-periodic infinite texturing of surfaces based on a single image. Pattern repetition is avoided by defining patches within each texture whose content can be changed at runtime. In addition, we consistently manage multi...

Two‐dimensional (2D) parametric colour functions are widely used in Image‐Based Rendering and Image Relighting. They make it possible to express the colour of a point depending on a continuous directional parameter: the viewing or the incident light direction. Producing such functions from acquired data is promising but difficult. Indeed, an intens...