Material appearance is a key component of photorealism, with a pronounced impact on human perception. Although there are many prior works targeting at measuring opaque materials using light-weight setups (e.g., consumer-level cameras), little attention is paid on acquiring the optical properties of translucent materials which are also quite common in nature. In this paper, we present a practical method for acquiring scattering properties of translucent materials, based solely on ordinary images captured with unknown lighting and camera parameters. The key to our method is an inter-pixel translucency prior which states that image pixels of a given homogeneous translucent material typically form curves (dubbed translucent curves) in the RGB space, of which the shapes are determined by the parameters of the material. We leverage this prior in a specially-designed convolutional neural network comprising multiple encoders, a translucency-aware feature fusion module and a cascaded decoder. We demonstrate, through both visual comparisons and quantitative evaluations, that high accuracy can be achieved on a wide range of real-world translucent materials.

Although neural supersampling has achieved great success in various applications for improving image quality, it is still difficult to apply it to a wide range of real-time rendering applications due to the high computational power demand. Most existing methods are computationally expensive and require high-performance hardware, preventing their use on platforms with limited hardware, such as smartphones. To this end, we propose a new supersampling framework for real-time rendering applications to reconstruct a high-quality image out of a low-resolution one, which is sufficiently lightweight to run on smartphones within a real-time budget. Our model takes as input the renderer-generated low resolution content and produces high resolution and anti-aliased results. To maximize sampling efficiency, we propose using an alternate sub-pixel sample pattern during the rasterization process. This allows us to create a relatively small reconstruction model while maintaining high image quality. By accumulating new samples into a high-resolution history buffer, an efficient history check and re-usage scheme is introduced to improve temporal stability. To our knowledge, this is the first research in pushing real-time neural supersampling on mobile devices. Due to the absence of training data, we present a new dataset containing 57 training and test sequences from three game scenes. Furthermore, based on the rendered motion vectors and a visual perception study, we introduce a new metric called inter-frame structural similarity (IF-SSIM) to quantitatively measure the temporal stability of rendered videos. Extensive evaluations demonstrate that our supersampling model outperforms existing or alternative solutions in both performance and temporal stability.

Video deduplication in cloud and on devices is a key challenge for storage and communication efficiency. The lifetime of video content creation, communication/sharing, and consumption can generate multiple versions of the same content with variations in coding and editing effects. In this work, we develop a lightweight and robust deduplication feature based on the fisher vector aggregation of Scale-Invariant Feature Transform (SIFT) keypoints. The fisher vector representation is used for a deduplication transfer learning process that utilizes a lightweight Multilayer Perceptron (MLP) network with center loss to learn a compact and distinctive feature. Simulation on the CC_WEB_VIDEO dataset demonstrated that the proposed feature is extremely robust in deduplication with respect to typical editing effects and coding/transcoding degenerations while being computationally very lightweight compared to other solutions.

With the increasing reliance of complex real-world applications on composite web services assembled from independently developed component services, there is a growing need for effective approaches to verifying that a composite service not only offers the required functionality but also satisfies the desired non-functional requirements (NFRs). In high-assurance applications such as traffic control, medical decision support, and coordinated response to civil emergencies, of special concern are NFRs having to do with security, safety and reliability of composite services. Current approaches to verifying NFRs of composite services (as opposed to individual services) remain largely ad-hoc and informal in nature. In this paper we develop techniques for ensuring that a composite service meets the user-specified NFRs expressible in the form of hard constraints e.g., “response time has to be less than 5 minutes.” We introduce an automata-based framework for verifying that a composite service satisfies the desired NFRs based on the known guarantees regarding the non-functional properties of the component services. We further show how to improve the efficiency of verifying that a composite service indeed satisfies a desired set of NFRs by: (i) Exploiting information about the applicability of specific NFRs (e.g., security) only to certain subsets of the component services that make up a composite service to minimize the verification effort and (ii) Identifying inconsistencies between NFRs with overlapping scopes. We illustrate how our approach can be used to verify the security requirements for an Emergency Management System. We also show how the approach can be used to verify whether a composite service satisfies any desired set of NFRs that can be expressed in the form of hard constraints of a quantitative nature.