Matchmaking connects multiple players to participate in online player-versus-player games. Current matchmaking systems depend on a single core strategy: create fair games at all times. These systems pair similarly skilled players on the assumption that a fair game is best player experience. We will demonstrate, however, that this intuitive assumption sometimes fails and that matchmaking based on fairness is not optimal for engagement.

Texture is an essential feature in modeling the appearance of objects and is instrumental in making virtual objects appear interesting and/or realistic. Unfortunately, obtaining textures is a labor intensive task requiring parameter tuning for procedural methods or careful photography and post-processing for natural images. Many texture synthesis techniques have been developed to generate textures of arbitrary spatial extent, but these techniques require the user to first produce an exemplar consisting solely of the desired texture. We present a fast method using diffusion manifolds to locate textures in unconstrained photographs, and extract exemplar tiles. The method requires the user to only specify a single point within the image containing the desired texture and the scale of the desired texture. The user may tune the result using simple interactions. The method is non-local, in the sense that the desired texture does not have to appear in a single contiguous region in the source image. This document is a summary of our work and system. A full account is available online.

Abstract Directors employ a process called “color grading” to add color styles to feature films. Color grading is used for a number of reasons, such as accentuating a certain emotion or expressing the signature look of a director. We collect a database of feature film clips and label them with tags such as director, emotion, and genre. We then learn a model that maps from the low‐level color and tone properties of film clips to the associated labels. This model allows us to examine a number of common hypotheses on the use of color to achieve goals, such as specific emotions. We also describe a method to apply our learned color styles to new images and videos. Along with our analysis of color grading techniques, we demonstrate a number of images and videos that are automatically filtered to resemble certain film styles.

Compositing is one of the most commonly performed operations in computer graphics. A realistic composite requires adjusting the appearance of the foreground and background so that they appear compatible; unfortunately, this task is challenging and poorly understood. We use statistical and visual perception experiments to study the realism of image composites. First, we evaluate a number of standard 2D image statistical measures, and identify those that are most significant in determining the realism of a composite. Then, we perform a human subjects experiment to determine how the changes in these key statistics influence human judgements of composite realism. Finally, we describe a data-driven algorithm that automatically adjusts these statistical measures in a foreground to make it more compatible with its background in a composite. We show a number of compositing results, and evaluate the performance of both our algorithm and previous work with a human subjects study.

Abstract The appearance of weathering effects on stone is important for creating outdoor scenes in computer graphics. To achieve them, previous research has built upon physical simulation, which, while yielding a degree of realism, is computationally expensive and inapplicable to the situation when the object geometry is unknown. Also, physical simulation requires specific knowledge of the stone properties and environmental processes. In this paper, we present a simple visual simulation pipeline for creating weathering effects on stone within a single image. Two primary effects of stone weathering, i.e., smoothing and roughening, are considered. In addition, erosion on the object silhouette is treated. These challenging effects involve significant geometry changes, which are intractable for previous image‐based editing techniques. The effectiveness of our technique is illustrated on a variety of scenes and types of stone. While it can be fully automatic, it also allows easy user interaction.

Abstract Papercraft models can serve as inexpensive prototypes in shape design applications. However, in making the models some geometric detail is necessarily lost, and artificial creases may be visible, thereby limiting the utility of these models. To compensate for these practical limitations, we introduce the use of printed patterns on papercraft models to enhance the perception of the shape they are intended to represent. We propose pattern generation schemes that modulate the sizes, directions, and densities of glyphs of patterns based on geometric attributes. We present a psychophysical experiment designed to explore the effect that printed patterns have on the perception of the papercraft model shapes. We find that models with printed patterns are perceived to represent the intended shape more accurately, and, further, that the type of printed pattern has an impact on the perceived shape.

Abstract We propose a new texture editing operation called texture splicing. For this operation, we regard a texture as having repetitive elements (textons) seamlessly distributed in a particular pattern. Taking two textures as input, texture splicing generates a new texture by selecting the texton appearance from one texture and distribution from the other. Texture splicing involves self‐similarity search to extract the distribution, distribution warping, context‐dependent warping, and finally, texture refinement to preserve overall appearance. We show a variety of results to illustrate this operation.

Abstract The appearance manifold [WTL*06] is an efficient approach for modeling and editing time‐variant appearance of materials from the BRDF data captured at single time instance. However, this method is difficult to apply in images in which weathering and shading variations are combined. In this paper, we present a technique for modeling and editing the weathering effects of an object in a single image with appearance manifolds. In our approach, we formulate the input image as the product of reflectance and illuminance. An iterative method is then developed to construct the appearance manifold in color space (i.e., Lab space) for modeling the reflectance variations caused by weathering. Based on the appearance manifold, we propose a statistical method to robustly decompose reflectance and illuminance for each pixel. For editing, we introduce a “pixel‐walking” scheme to modify the pixel reflectance according to its position on the manifold, by which the detailed reflectance variations are well preserved. We illustrate our technique in various applications, including weathering transfer between two images that is first enabled by our technique. Results show that our technique can produce much better results than existing methods, especially for objects with complex geometry and shading effects.

Image based rendering (IBR) usually produces severe artifacts, typically blur and ghost, for objects not located on the focal plane. To obtain an all clear result, previous works either endeavor to recover scene depth or rest on iterations. These methods are difficult and time-consuming, not suitable for real-time IBR applications. In this paper, we propose a novel all-clear synthesis scheme bypassing tedious depth estimation. Our algorithm directly measures the clarity degree of local regions in pre-rendered images and then optimal blocks are combined. This measurement is motivated by the observation that mean change energies (MCE) are well consistent with the human vision system's feeling of "clarity". Furthermore, an efficient pseudo-depth filtering is proposed to alleviate the block effect during combination. Our algorithm gives outstanding performances on both synthetic and real world data. It is also proved to be fast enough for real-time implementation.

While most image completion methods focus on filling regions with structures or stationary textures, few are suitable for completing large-scale missing parts on complex background with nonlinearly progressive color changes. In this paper, we propose a novel approach, termed as nonlinear Poisson completion, to solve this problem. The visible parts of the background serve as a training set, from which we learn the embedding nonlinear subspace of progressive colors, namely color manifold. A Poisson image completion procedure, which works efficiently for smoothly linear interpolation, is extended to nonlinearly recover the missing regions with iteration solution confined to the manifold. In some especially challenging cases, a simple post-processing serves to generate more natural-looking results. Experiments on both synthetic and real images verify the effectiveness of the proposed algorithm.

Compression and calculation efficiency are two key problems for light field coding. While nearly all works focus on one quality and neglect the other, this paper introduces a novel color block truncation algorithm using hierarchical bit-plane prediction (CBTC-HBPP) to chase for them both. This approach first performs two-color-truncation in block classes extracted from the whole light field. The yielding bit-plane and a const index table make decoding and rendering easy and fast. Hierarchical bit-plane prediction is employed to compressed bit-plane further. Different from motion and disparity estimation, this bit prediction has only a two-value result and it can be carried out fast. Both moderate compression and calculation efficiency are achieved.