Unsupervised Deep Single-Image Intrinsic Decomposition using Illumination-Varying Image Sequences

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 ourselves from this limitation by training on unannotated images. Our method leverages the observation that two images of the same scene but with different lighting provide useful information on their intrinsic properties: by definition, albedo is invariant to lighting conditions, and cross-combining the estimated albedo of a first image with the estimated shading of a second one should lead back to the second one's input image. We transcribe this relationship into a siamese training scheme for a deep convolutional neural network that decomposes a single image into albedo and shading. The siamese setting allows us to introduce a new loss function including such cross-combinations, and to train solely on (time-lapse) images, discarding the need for any ground truth annotations. As a result, our method has the good properties of i) taking advantage of the time-varying information of image sequences in the (pre-computed) training step, ii) not requiring ground truth data to train on, and iii) being able to decompose single images of unseen scenes at runtime. To demonstrate and evaluate our work, we additionally propose a new rendered dataset containing illumination-varying scenes and a set of quantitative metrics to evaluate SIID algorithms. Despite its unsupervised nature, our results compete with state of the art methods, including supervised and non data-driven methods.Comment: To appear in Pacific Graphics 201

Computational rim illumination of dynamic subjects using aerial robots

Lighting plays a major role in photography. Professional photographers use elaborate installations to light their subjects and achieve sophisticated styles. However, lighting moving subjects performing dynamic tasks presents significant challenges and requires expensive manual intervention. A skilled additional assistant might be needed to reposition lights as the subject changes pose or moves, and the extra logistics significantly raises costs and time. The associated latencies as the assistant lights the subject, and the communication required from the photographer to achieve optimum lighting could mean missing a critical shot. We present a new approach to lighting dynamic subjects where an aerial robot equipped with a portable light source lights the subject to automatically achieve a desired lighting effect. We focus on rim lighting, a particularly challenging effect to achieve with dynamic subjects, and allow the photographer to specify a required rim width. Our algorithm processes the images from the photographer׳s camera and provides necessary motion commands to the aerial robot to achieve the desired rim width in the resulting photographs. With an indoor setup, we demonstrate a control approach that localizes the aerial robot with reference to the subject and tracks the subject to achieve the necessary motion. In addition to indoor experiments, we perform open-loop outdoor experiments in a realistic photo-shooting scenario to understand lighting ergonomics. Our proof-of-concept results demonstrate the utility of robots in computational lighting

A Dataset of Multi-Illumination Images in the Wild

Collections of images under a single, uncontrolled illumination have enabled the rapid advancement of core computer vision tasks like classification, detection, and segmentation. But even with modern learning techniques, many inverse problems involving lighting and material understanding remain too severely ill-posed to be solved with single-illumination datasets. To fill this gap, we introduce a new multi-illumination dataset of more than 1000 real scenes, each captured under 25 lighting conditions. We demonstrate the richness of this dataset by training state-of-the-art models for three challenging applications: single-image illumination estimation, image relighting, and mixed-illuminant white balance.Comment: ICCV 201

Image manipulation: Photoshop as a data-measurement tool

Researchers believe that image manipulation threatens photography\u27s perceived objectivity of capturing moments in history. Current research exists that is aimed at determining whether images have been subjected to methods of manipulation. While this research is thorough in its approaches to detection, it lacks methods that would facilitate the measurement of those manipulations; This study uses Photoshop to measure the qualitative changes in images. The aesthetic dimensions set forth by Gillian Rose (2007) such as content, color, spatial organization, and light can be isolated, manipulated, and ultimately measured; This research is aimed at facilitating additional questions regarding what constitutes image manipulation, the extent of image manipulation using the methods described herein, and how image manipulation may affect the viewer. It also hopes to show that widely accepted practices of image modification need to be revisited as technologies continue to update at an unprecedented rate

Image-based Material Editing

Photo editing software allows digital images to be blurred, warped or re-colored at the touch of a button. However, it is not currently possible to change the material appearance of an object except by painstakingly painting over the appropriate pixels. Here we present a set of methods for automatically replacing one material with another, completely different material, starting with only a single high dynamic range image, and an alpha matte specifying the object. Our approach exploits the fact that human vision is surprisingly tolerant of certain (sometimes enormous) physical inaccuracies. Thus, it may be possible to produce a visually compelling illusion of material transformations, without fully reconstructing the lighting or geometry. We employ a range of algorithms depending on the target material. First, an approximate depth map is derived from the image intensities using bilateral filters. The resulting surface normals are then used to map data onto the surface of the object to specify its material appearance. To create transparent or translucent materials, the mapped data are derived from the object\u27s background. To create textured materials, the mapped data are a texture map. The surface normals can also be used to apply arbitrary bidirectional reflectance distribution functions to the surface, allowing us to simulate a wide range of materials. To facilitate the process of material editing, we generate the HDR image with a novel algorithm, that is robust against noise in individual exposures. This ensures that any noise, which would possibly have affected the shape recovery of the objects adversely, will be removed. We also present an algorithm to automatically generate alpha mattes. This algorithm requires as input two images--one where the object is in focus, and one where the background is in focus--and then automatically produces an approximate matte, indicating which pixels belong to the object. The result is then improved by a second algorithm to generate an accurate alpha matte, which can be given as input to our material editing techniques

Plausible Shading Decomposition For Layered Photo Retouching

Photographers routinely compose multiple manipulated photos of the same scene (layers) into a single image, which is better than any individual photo could be alone. Similarly, 3D artists set up rendering systems to produce layered images to contain only individual aspects of the light transport, which are composed into the final result in post-production. Regrettably, both approaches either take considerable time to capture, or remain limited to synthetic scenes. In this paper, we suggest a system to allow decomposing a single image into a plausible shading decomposition (PSD) that approximates effects such as shadow, diffuse illumination, albedo, and specular shading. This decomposition can then be manipulated in any off-the-shelf image manipulation software and recomposited back. We perform such a decomposition by learning a convolutional neural network trained using synthetic data. We demonstrate the effectiveness of our decomposition on synthetic (i.e., rendered) and real data (i.e., photographs), and use them for common photo manipulation, which are nearly impossible to perform otherwise from single images

The Virtual Worlds of Cinema Visual Effects, Simulation, and the Aesthetics of Cinematic Immersion

This thesis develops a phenomenology of immersive cinematic spectatorship. During an immersive experience in the cinema, the images, sounds, events, emotions, and characters that form a fictional diegesis become so compelling that our conscious experience of the real world is displaced by a virtual world. Theorists and audiences have long recognized cinema’s ability to momentarily substitute for the lived experience of reality, but it remains an under-theorized aspect of cinematic spectatorship. The first aim of this thesis is therefore to examine these immersive responses to cinema from three perspectives – the formal, the technological, and the neuroscientific – to describe the exact mechanisms through which a spectator’s immersion in a cinematic world is achieved. A second aim is to examine the historical development of the technologies of visual simulation that are used to create these immersive diegetic worlds. My analysis shows a consistent increase in the vividness and transparency of simulative technologies, two factors that are crucial determinants in a spectator’s immersion. In contrast to the cultural anxiety that often surrounds immersive responses to simulative technologies, I examine immersive spectatorship as an aesthetic phenomenon that is central to our engagement with cinema. The ubiquity of narrative – written, verbal, cinematic – shows that the ability to achieve immersion is a fundamental property of the human mind found in cultures diverse in both time and place. This thesis is thus an attempt to illuminate this unique human ability and examine the technologies that allow it to flourish

Time-varying volume visualization

Volume rendering is a very active research field in Computer Graphics because of its wide range of applications in various sciences, from medicine to flow mechanics. In this report, we survey a state-of-the-art on time-varying volume rendering. We state several basic concepts and then we establish several criteria to classify the studied works: IVR versus DVR, 4D versus 3D+time, compression techniques, involved architectures, use of parallelism and image-space versus object-space coherence. We also address other related problems as transfer functions and 2D cross-sections computation of time-varying volume data. All the papers reviewed are classified into several tables based on the mentioned classification and, finally, several conclusions are presented.Preprin

Re-animating Climate Change: Abstract Temporalities in Augmented Reality

This article explores how animation and augmented reality (AR) can create compression and re-distribution of moving image to convey the temporal scales at play in climate change. Animation inherently fosters experimentation with the expression and understanding of time. AR combines the temporal quality of animation with the physical environment, creating a hybrid space of moving image, technology and physical objects that operate on different time scales. This presents an opportunity to engage imaginatively with aspects of climate change that science communication research has identified as problematic to comprehend, such as the immense timescale on which it occurs. My practice-based research explores techniques, including limited animation, AR image targets and layering of two-dimensional moving image in physical space, to demonstrate how these ideas can be implemented both in a gallery and in the natural environment