We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.

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Video cutout refers to extracting moving objects from videos, which is an important step in many video editing tasks. Recent algorithms have limitations in terms of efficiency, interaction style, and robustness. This paper presents a novel method for progressive video cutout with less user interaction and fast feedback. By exploring local and compact features, an optimization is constructed based on a graph model which establishes spatial and temporal relationship of neighboring patches in video frames. This optimization enables an efficient solution for progressive video cutout using graph cuts. Furthermore, a sampling-based method for temporally coherent matting is proposed to further refine video cutout results. Experiments demonstrate that our video cutout by paint selection is more intuitive and efficient for users than previous stroke-based methods, and thus could be put into practical use.

Many recent applications of computer graphics and human computer interaction have adopted both colour cameras and depth cameras as input devices. Therefore, an effective calibration of both types of hardware taking different colour and depth inputs is required. Our approach removes the numerical difficulties of using non-linear optimization in previous methods which explicitly resolve camera intrinsics as well as the transformation between depth and colour cameras. A matrix of hybrid parameters is introduced to linearize our optimization. The hybrid parameters offer a transformation from a depth parametric space (depth camera image) to a colour parametric space (colour camera image) by combining the intrinsic parameters of depth camera and a rotation transformation from depth camera to colour camera. Both the rotation transformation and intrinsic parameters can be explicitly calculated from our hybrid parameters with the help of a standard QR factorisation. We test our algorithm with both synthesized data and real-world data where ground-truth depth information is captured by Microsoft Kinect. The experiments show that our approach can provide comparable accuracy of calibration with the state-of-the-art algorithms while taking much less computation time (1/50 of Herrera's method and 1/10 of Raposo's method) due to the advantage of using hybrid parameters.

21One of the fundamental reactions of the innate immune responses to pathogen infection is the 22 release of pro-inflammatory cytokines, including IL-1β, processed by the NLRP3 inflammasome. 23 STING is essential for innate immune responses and inflammasome activation. Here we reveal a 24 distinct mechanism by which STING regulates the NLRP3 inflammasome activation, IL-1β 25 secretion, and inflammatory responses in human cell lines, mice primary cells, and mice. 26 Interestingly, upon HSV-1 infection and cytosolic DNA stimulation, STING binds to NLRP3 and 27 promotes the inflammasome activation through two approaches. First, STING recruits NLRP3 and 28 promotes NLRP3 translocation to the endoplasmic reticulum, thereby facilitating the inflammasome 29 formation. Second, STING interacts with NLRP3 and removes K48-and K63-linked 30 polyubiquitination of NLRP3, thereby promoting the inflammasome activation. Collectively, we 31 demonstrate that the cGAS-STING-NLRP3 signaling is essential for host defense against DNA 32 virus infection. 33 34 Keywords: Cyclic GMP-AMP synthase (cGAS)/Herpes simplex virus type 1 35 (HSV-1)/Interleukine-1(IL-1)/Polyubiquitination and deubiquitination/The cGAS-STING 36 pathway 37 38 3 75NLRP3 polyubiquitination, thereby promoting the inflammasome activation. We propose that the 76 cGAS-cGAMP-STING-NLRP3 axis is essential for host defense against DNA virus infection. 77 78 5 Results 79 80STING interacts with NLRP3 to facilitate the inflammasome activation. 81 We initially determined the correlation between STING and NLRP3, and showed that STING and 82 NLRP3 interacted with each other in human embryonic kidney (HEK293T) cells ( Fig 1A, B). The 83 NLRP3 inflammasome consists of three major components, NLRP3, ASC, and pro-Casp-1 84 (Schroder & Tschopp, 2010). We explored whether STING interacts with ASC and/or pro-Casp-1, 85 and clearly revealed that STING interacted with NLRP3, but not with ASC or pro-Casp-1 ( Fig 1C). 86 NLRP3 protein harbors several prototypic domains, including PYRIN domain (PYD), 87 NACHT-associated domain (NAD), and Leucine rich repeats (LRR) (Ye & Ting, 2008). Next, the 88 domain of NLRP3 involved in the interaction with STING was determined by evaluating the 89 plasmids encoding NLRP3, PYRIN, NACHT, or LRR (Figure 1D) as described previously (Wang et 90 al, 2018). Like NLRP3, NACHT and LRR interacted with STING, but PYRIN failed to interact 91 with STING (Figure 1E), and consistently, STING interacted with NLRP3, NACHT, and LRR (Fig 92 1F, lanes 2, 6 and 8), but not with PYRIN (Fig 1F, lane 4). In another hand, STING comprises five 93 putative transmembrane (TM) regions (Ishikawa & Barber, 2008). The domain of STING required 94for the interaction with NLRP3 was assessed by analyzing plasmids encoding wild-type (WT) 95 STING and seven truncated proteins ( Fig 1G). Like WT STING(1-379aa) ( Fig 1H, lane 9), the 96 truncated proteins STING(1-160aa), STING(1-240aa), STING(41-379aa), STING(81-379aa), and 97 STING(111-379aa) interacted strongly with NLRP3 ( Fig...