Self-powered MSM solar-blind AlGaN photodetector realized by in-plane polarization modulation.

Solid-state self-powered UV detection is strongly required in various application fields to enable long-term operation. However, this requirement is incompatible with conventionally used metal-semiconductor-metal (MSM) UV photodetectors (PDs) due to the symmetric design of Schottky contacts. In this work, a self-powered MSM solar-blind UV-PD was realized using a lateral pn junction architecture.

Pose-Aware Attention Network for Flexible Motion Retargeting by Body Part.

Motion retargeting is a fundamental problem in computer graphics and computer vision. Existing approaches usually have many strict requirements, such as the source-target skeletons needing to have the same number of joints or share the same topology. To tackle this problem, we note that skeletons with different structure may have some common body parts despite the differences in joint numbers.

Motif-GCNs With Local and Non-Local Temporal Blocks for Skeleton-Based Action Recognition.

Recent works have achieved remarkable performance for action recognition with human skeletal data by utilizing graph convolutional models. Existing models mainly focus on developing graph convolutional operations to encode structural properties of a skeletal graph, whose topology is manually predefined and fixed over all action samples. Some recent works further take sample-dependent relationships among joints into consideration.

Multiscale Mesh Deformation Component Analysis With Attention-Based Autoencoders.

Deformation component analysis is a fundamental problem in geometry processing and shape understanding. Existing approaches mainly extract deformation components in local regions at a similar scale while deformations of real-world objects are usually distributed in a multi-scale manner. In this article, we propose a novel method to exact multiscale deformation components automatically with a stacked attention-based autoencoder.

Upregulation of endothelial cell-derived exosomal microRNA-125b-5p protects from sepsis-induced acute lung injury by inhibiting topoisomerase II alpha.

Objective: Emerging evidence has revealed that exosomal microRNAs (miRNAs) are implicated in human diseases. However, role of exosomal miR-125b-5p in sepsis-induced acute lung injury (ALI) remains further explored. We focused on the effect of exosomal miR-125b-5p on ALI progression via targeting topoisomerase II alpha (TOP2A).

Active Colorization for Cartoon Line Drawings.

In the animation industry, the colorization of raw sketch images is a vitally important but very time-consuming task. This article focuses on providing a novel solution that semiautomatically colorizes a set of images using a single colorized reference image. Our method is able to provide coherent colors for regions that have similar semantics to those in the reference image.

Weakly Supervised Adversarial Learning for 3D Human Pose Estimation from Point Clouds.

Point clouds-based 3D human pose estimation that aims to recover the 3D locations of human skeleton joints plays an important role in many AR/VR applications. The success of existing methods is generally built upon large scale data annotated with 3D human joints. However, it is a labor-intensive and error-prone process to annotate 3D human joints from input depth images or point clouds, due to the self-occlusion between body parts as well as the tedious annotation process on 3D point clouds.

Sparse Data Driven Mesh Deformation.

Example-based mesh deformation methods are powerful tools for realistic shape editing. However, existing techniques typically combine all the example deformation modes, which can lead to overfitting, i.e.

Combining Recurrent Neural Networks and Adversarial Training for Human Motion Synthesis and Control.

This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method for training an RNN model from prerecorded motion data.

Realtime and Accurate 3D Eye Gaze Capture with DCNN-based Iris and Pupil Segmentation.

This paper presents a realtime and accurate method for 3D eye gaze tracking with a monocular RGB camera. Our key idea is to train a deep convolutional neural network(DCNN) that automatically extracts the iris and pupil pixels of each eye from input images. To achieve this goal, we combine the power of Unet\cite{ronneberger2015u-net:} and Squeezenet\cite{iandola2017squeezenet:} to train an efficient convolutional neural network for pixel classification.

Temporal Upsampling of Depth Maps Using a Hybrid Camera.

In recent years, consumer-level depth cameras have been adopted for various applications. However, they often produce depth maps at only a moderately high frame rate (approximately 30 frames per second), preventing them from being used for applications such as digitizing human performance involving fast motion. On the other hand, low-cost, high-frame-rate video cameras are available.

Effect of tibia marker placement on knee joint kinematic analysis.

Variability of kinematic measures determined by different marker sets among sites participating in a collaborative study is necessary for determining the reliability of a multi-site gait analysis research. We compared knee kinematics based on different marker sets on the tibia, calculating by segmental optimization (SO) and multi-body optimization (MBO) methods respectively, in order to assess the effect of marker locations on the methods. 11 healthy subjects participated in the study with 33 markers attached to the lower extremity segments, and 4 groups were identified according to markers on the tibia.

Templateless Non-Rigid Reconstruction and Motion Tracking With a Single RGB-D Camera.

We present a novel templateless approach for nonrigid reconstruction and motion tracking using a single RGB-D camera. Without any template prior, our system achieves accurate reconstruction and tracking for considerably deformable objects. To robustly register the input sequence of partial depth scans with dynamic motion, we propose an efficient local-to-global hierarchical optimization framework inspired by the idea of traditional structure-from-motion.