Efficient rendering of photo‐realistic virtual worlds is a long standing effort of computer graphics. Modern graphics techniques have succeeded in synthesizing photo‐realistic images from hand‐crafted scene representations. However, the automatic generation of shape, materials, lighting, and other aspects of scenes remains a challenging problem that, if solved, would make photo‐realistic computer graphics more widely accessible. Concurrently, progress in computer vision and machine learning have given rise to a new approach to image synthesis and editing, namely deep generative models. Neural rendering is a new and rapidly emerging field that combines generative machine learning techniques with physical knowledge from computer graphics, e.g., by the integration of differentiable rendering into network training. With a plethora of applications in computer graphics and vision, neural rendering is poised to become a new area in the graphics community, yet no survey of this emerging field exists. This state‐of‐the‐art report summarizes the recent trends and applications of neural rendering. We focus on approaches that combine classic computer graphics techniques with deep generative models to obtain controllable and photorealistic outputs. Starting with an overview of the underlying computer graphics and machine learning concepts, we discuss critical aspects of neural rendering approaches. Specifically, our emphasis is on the type of control, i.e., how the control is provided, which parts of the pipeline are learned, explicit vs. implicit control, generalization, and stochastic vs. deterministic synthesis. The second half of this state‐of‐the‐art report is focused on the many important use cases for the described algorithms such as novel view synthesis, semantic photo manipulation, facial and body reenactment, relighting, free‐viewpoint video, and the creation of photo‐realistic avatars for virtual and augmented reality telepresence. Finally, we conclude with a discussion of the social implications of such technology and investigate open research problems.

We explore total scene capture -recording, modeling, and rerendering a scene under varying appearance such as season and time of day. Starting from internet photos of a tourist landmark, we apply traditional 3D reconstruction to register the photos and approximate the scene as a point cloud. For each photo, we render the scene points into a deep framebuffer, and train a neural network to learn the mapping of these initial renderings to the actual photos. This rerendering network also takes as input a latent appearance vector and a semantic mask indicating the location of transient objects like pedestrians. The model is evaluated on several datasets of publicly available images spanning a broad range of illumination conditions. We create short videos demonstrating realistic manipulation of the image viewpoint, appearance, and semantic labeling. We also compare results with prior work on scene reconstruction from internet photos.

To enhance methane production in situ in bituminous coal seams, distribution of microorganisms in the formation water collected from a coalbed methane well was investigated. Based on next generation DNA sequencing, both bacteria (231 species) and archaea (33 species) were identified. Among the bacterial kingdom, polymerdegrading, benzoate, fatty acid and sugar utilizing bacteria were dominant. Among the archaea domain, the major methanogens (89.8%) belonged to the order of Methanobacteriales which are hydrogenotrophic. To develop a microbial consortium for ex situ coal bioconversion, the original microbial community was adapted to ground coals for five months in a laboratory environment. DNA sequencing revealed the presence of 185 bacteria species and nine archaea species which were dramatically different from those in the original formation water. In particular, the majority (90.4%) of methanogens were under the order of Methanomicrobiales. To increase methane production, two nutrient solutions were tested. Solution #2 which targeted methanogens provided a methane yield of 111 ft 3 /ton in 20 days, which translated to a 5.6 ft 3 /ton-day. In addition, the adapted consortium was found to be aerotolerant.Published by Elsevier B.V.

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Fig. 1. The results of our portrait enhancement method on real-world portrait photographs. Casual portrait photographs often suffer from undesirable shadows, particularly foreign shadows cast by external objects, and dark facial shadows cast by the face upon itself under harsh illumination. We propose an automated technique for enhancing these poorly-lit portrait photographs by removing unwanted foreign shadows, reducing harsh facial shadows, and adding synthetic fill lights. Casually-taken portrait photographs often suffer from unflattering lighting and shadowing because of suboptimal conditions in the environment. Aesthetic qualities such as the position and softness of shadows and the lighting ratio between the bright and dark parts of the face are frequently determined by the constraints of the environment rather than by the photographer. Professionals address this issue by adding light shaping tools such as scrims, bounce cards, and flashes. In this paper, we present a computational approach that gives casual photographers some of this control, thereby