You Only Train Once: Multi-Identity Free-Viewpoint Neural Human Rendering from Monocular Videos

We introduce You Only Train Once (YOTO), a dynamic human generation framework, which performs free-viewpoint rendering of different human identities with distinct motions, via only one-time training from monocular videos. Most prior works for the task require individualized optimization for each input video that contains a distinct human identity, leading to a significant amount of time and resources for the deployment, thereby impeding the scalability and the overall application potential of the system. In this paper, we tackle this problem by proposing a set of learnable identity codes to expand the capability of the framework for multi-identity free-viewpoint rendering, and an effective pose-conditioned code query mechanism to finely model the pose-dependent non-rigid motions. YOTO optimizes neural radiance fields (NeRF) by utilizing designed identity codes to condition the model for learning various canonical T-pose appearances in a single shared volumetric representation. Besides, our joint learning of multiple identities within a unified model incidentally enables flexible motion transfer in high-quality photo-realistic renderings for all learned appearances. This capability expands its potential use in important applications, including Virtual Reality. We present extensive experimental results on ZJU-MoCap and PeopleSnapshot to clearly demonstrate the effectiveness of our proposed model. YOTO shows state-of-the-art performance on all evaluation metrics while showing significant benefits in training and inference efficiency as well as rendering quality. The code and model will be made publicly available soon

Image Anomaly Detection and Localization with Position and Neighborhood Information

Anomaly detection and localization are essential in many areas, where collecting enough anomalous samples for training is almost impossible. To overcome this difficulty, many existing methods use a pre-trained network to encode input images and non-parametric modeling to estimate the encoded feature distribution. In the modeling process, however, they overlook that position and neighborhood information affect the distribution of normal features. To use the information, in this paper, the normal distribution is estimated with conditional probability given neighborhood features, which is modeled with a multi-layer perceptron network. At the same time, positional information can be used by building a histogram of representative features at each position. While existing methods simply resize the anomaly map into the resolution of an input image, the proposed method uses an additional refine network that is trained from synthetic anomaly images to perform better interpolation considering the shape and edge of the input image. For the popular industrial dataset, MVTec AD benchmark, the experimental results show \textbf{99.52\%} and \textbf{98.91\%} AUROC scores in anomaly detection and localization, which is state-of-the-art performance

Homotopy Reconstruction via the Cech Complex and the Vietoris-Rips Complex

We derive conditions under which the reconstruction of a target space is topologically correct via the ?ech complex or the Vietoris-Rips complex obtained from possibly noisy point cloud data. We provide two novel theoretical results. First, we describe sufficient conditions under which any non-empty intersection of finitely many Euclidean balls intersected with a positive reach set is contractible, so that the Nerve theorem applies for the restricted ?ech complex. Second, we demonstrate the homotopy equivalence of a positive ?-reach set and its offsets. Applying these results to the restricted ?ech complex and using the interleaving relations with the ?ech complex (or the Vietoris-Rips complex), we formulate conditions guaranteeing that the target space is homotopy equivalent to the ?ech complex (or the Vietoris-Rips complex), in terms of the ?-reach. Our results sharpen existing results

Uniform Convergence of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension

51 pagesInternational audienceWe derive concentration inequalities for the supremum norm of the difference between a kernel density estimator (KDE) and its point-wise expectation that hold uniformly over the selection of the bandwidth and under weaker conditions on the kernel and the data generating distribution than previously used in the literature. We first propose a novel concept, called the volume dimension, to measure the intrinsic dimension of the support of a probability distribution based on the rates of decay of the probability of vanishing Euclidean balls. Our bounds depend on the volume dimension and generalize the existing bounds derived in the literature. In particular, when the data-generating distribution has a bounded Lebesgue density or is supported on a sufficiently well-behaved lower-dimensional manifold, our bound recovers the same convergence rate depending on the intrinsic dimension of the support as ones known in the literature. At the same time, our results apply to more general cases, such as the ones of distribution with unbounded densities or supported on a mixture of manifolds with different dimensions. Analogous bounds are derived for the derivative of the KDE, of any order. Our results are generally applicable but are especially useful for problems in geometric inference and topological data analysis, including level set estimation, density-based clustering, modal clustering and mode hunting, ridge estimation and persistent homology

Characterizing the Efficiency of Perovskite Solar Cells and Light-Emitting Diodes

Metal halide perovskites (MHPs) are being widely studied as a light-absorber for high-efficiency solar cells. With efforts being made throughout the globe, the power conversion efficiency of MHP solar cells has recently soared up to 25.2%. MHPs are now being spotlighted as a next-generation light-emitter as well. Their high color purity and solution-processability are of particular interest for display applications, which in general benefit from wide color gamut and low-cost high-resolution subpixel patterning. For this reason, research activities on perovskite light-emitting diodes (LEDs) are rapidly growing, and their external quantum efficiencies have been dramatically improved to over 20%. As more and more research groups with different backgrounds are working on these perovskite optoelectronic devices, the demand is growing for standard methods for accurate efficiency measurement that can be agreed upon across the disciplines and, at the same time, can be realized easily in the lab environment with due diligence. Herein, optoelectronic characterization methods are revisited from the viewpoint of MHP solar cells and LEDs. General efficiency measurement practices are first reviewed, common sources of errors are introduced, and guidelines for avoiding or minimizing those errors are then suggested to help researchers in fields develop the best measurement practice.

Efficient Perovskite Light-Emitting Diodes Using Polycrystalline Core-Shell-Mimicked Nanograins

Making small nanograins in polycrystalline organic-inorganic halide perovskite (OIHP) films is critical to improving the luminescent efficiency in perovskite light-emitting diodes (PeLEDs). 3D polycrystalline OIHPs have fundamental limitations related to exciton binding energy and exciton diffusion length. At the same time, passivating the defects at the grain boundaries is also critical when the grain size becomes smaller. Molecular additives can be incorporated to shield the nanograins to suppress defects at grain boundaries; however, unevenly distributed molecular additives can cause imbalanced charge distribution and inefficient local defect passivation in polycrystalline OIHP films. Here, a kinetically controlled polycrystalline organic-shielded nanograin (OSN) film with a uniformly distributed organic semiconducting additive (2,2 ',2 ''-(1,3,5-benzinetriyl)-tris(1-phenyl-1-H-benzimidazole), TPBI) is developed mimicking core-shell nanoparticles. The OSN film causes improved photophysical and electroluminescent properties with improved light out-coupling by possessing a low refractive index. Finally, highly improved electroluminescent efficiencies of 21.81% ph el(-1) and 87.35 cd A(-1) are achieved with a half-sphere lens and four-time increased half-lifetime in polycrystalline PeLEDs. This strategy to make homogeneous, defect-healed polycrystalline core-shell-mimicked nanograin film with better optical out-coupling will provide a simple and efficient way to make highly efficient perovskite polycrystal films and their optoelectronics devices.

Penilaian Kinerja Keuangan Koperasi di Kabupaten Pelalawan

This paper describe development and financial performance of cooperative in District Pelalawan among 2007 - 2008. Studies on primary and secondary cooperative in 12 sub-districts. Method in this stady use performance measuring of productivity, efficiency, growth, liquidity, and solvability of cooperative. Productivity of cooperative in Pelalawan was highly but efficiency still low. Profit and income were highly, even liquidity of cooperative very high, and solvability was good

Juxtaposing BTE and ATE – on the role of the European insurance industry in funding civil litigation

One of the ways in which legal services are financed, and indeed shaped, is through private insurance arrangement. Two contrasting types of legal expenses insurance contracts (LEI) seem to dominate in Europe: before the event (BTE) and after the event (ATE) legal expenses insurance. Notwithstanding institutional differences between different legal systems, BTE and ATE insurance arrangements may be instrumental if government policy is geared towards strengthening a market-oriented system of financing access to justice for individuals and business. At the same time, emphasizing the role of a private industry as a keeper of the gates to justice raises issues of accountability and transparency, not readily reconcilable with demands of competition. Moreover, multiple actors (clients, lawyers, courts, insurers) are involved, causing behavioural dynamics which are not easily predicted or influenced. Against this background, this paper looks into BTE and ATE arrangements by analysing the particularities of BTE and ATE arrangements currently available in some European jurisdictions and by painting a picture of their respective markets and legal contexts. This allows for some reflection on the performance of BTE and ATE providers as both financiers and keepers. Two issues emerge from the analysis that are worthy of some further reflection. Firstly, there is the problematic long-term sustainability of some ATE products. Secondly, the challenges faced by policymakers that would like to nudge consumers into voluntarily taking out BTE LEI

Search for stop and higgsino production using diphoton Higgs boson decays

Results are presented of a search for a "natural" supersymmetry scenario with gauge mediated symmetry breaking. It is assumed that only the supersymmetric partners of the top-quark (stop) and the Higgs boson (higgsino) are accessible. Events are examined in which there are two photons forming a Higgs boson candidate, and at least two b-quark jets. In 19.7 inverse femtobarns of proton-proton collision data at sqrt(s) = 8 TeV, recorded in the CMS experiment, no evidence of a signal is found and lower limits at the 95% confidence level are set, excluding the stop mass below 360 to 410 GeV, depending on the higgsino mass