Recent advancements in technology have brought forth new forms of interactive
applications, such as the social metaverse, where end users interact with each
other through their virtual avatars. In such applications, precise full-body
tracking is essential for an immersive experience and a sense of embodiment
with the virtual avatar. However, current motion capture systems are not easily
accessible to end users due to their high cost, the requirement for special
skills to operate them, or the discomfort associated with wearable devices. In
this paper, we present MOVIN, the data-driven generative method for real-time
motion capture with global tracking, using a single LiDAR sensor. Our
autoregressive conditional variational autoencoder (CVAE) model learns the
distribution of pose variations conditioned on the given 3D point cloud from
LiDAR.As a central factor for high-accuracy motion capture, we propose a novel
feature encoder to learn the correlation between the historical 3D point cloud
data and global, local pose features, resulting in effective learning of the
pose prior. Global pose features include root translation, rotation, and foot
contacts, while local features comprise joint positions and rotations.
Subsequently, a pose generator takes into account the sampled latent variable
along with the features from the previous frame to generate a plausible current
pose. Our framework accurately predicts the performer's 3D global information
and local joint details while effectively considering temporally coherent
movements across frames. We demonstrate the effectiveness of our architecture
through quantitative and qualitative evaluations, comparing it against
state-of-the-art methods. Additionally, we implement a real-time application to
showcase our method in real-world scenarios. MOVIN dataset is available at
https://movin3d.github.io/movin_pg2023/.

本文介绍了使用单个激光雷达传感器进行实时动作捕捉的数据驱动生成方法 MOVIN，结合自回归条件变分自编码器 (CVAE) 模型和姿势生成器，通过学习 3D 点云与全局、局部姿势特征之间的相关性，实现了准确的 3D 全局信息和局部关节细节的预测，并考虑了帧间时间连贯性，通过定量和定性评估验证了我们的架构的有效性，并在真实场景中实现了实时应用。

MOVIN: 使用单个 LiDAR 的实时运动捕捉

MOVIN: Real-time Motion Capture using a Single LiDAR

Non-Intrusive Load Monitoring (NILM) seeks to save energy by estimating
individual appliance power usage from a single aggregate measurement. Deep
neural networks have become increasingly popular in attempting to solve NILM
problems. However most used models are used for Load Identification rather than
online Source Separation. Among source separation models, most use a
single-task learning approach in which a neural network is trained exclusively
for each appliance. This strategy is computationally expensive and ignores the
fact that multiple appliances can be active simultaneously and dependencies
between them. The rest of models are not causal, which is important for
real-time application. Inspired by Convtas-Net, a model for speech separation,
we propose Conv-NILM-net, a fully convolutional framework for end-to-end NILM.
Conv-NILM-net is a causal model for multi appliance source separation. Our
model is tested on two real datasets REDD and UK-DALE and clearly outperforms
the state of the art while keeping a significantly smaller size than the
competing models.

使用深度神经网络进行实时，多设备源分离的，全卷积的非侵入式负荷监测，优于现有技术

Conv-NILM-Net, 一种用于能量来源分离的因果和多器具模型

Conv-NILM-Net, a causal and multi-appliance model for energy source  separation

The development of the Automatic License Plate Recognition (ALPR) system has
received much attention for the English license plate. However, despite being
the sixth largest population around the world, no significant progress can be
tracked in the Bengali language countries or states for the ALPR system
addressing their more alarming traffic management with inadequate road-safety
measures. This paper reports a computationally efficient and reasonably
accurate Automatic License Plate Recognition (ALPR) system for Bengali
characters with a new end-to-end DNN model that we call Bengali License Plate
Network(BLPnet). The cascaded architecture for detecting vehicle regions prior
to vehicle license plate (VLP) in the model is proposed to eliminate false
positives resulting in higher detection accuracy of VLP. Besides, a lower set
of trainable parameters is considered for reducing the computational cost
making the system faster and more compatible for a real-time application. With
a Computational Neural Network (CNN)based new Bengali OCR engine and
word-mapping process, the model is characters rotation invariant, and can
readily extract, detect and output the complete license plate number of a
vehicle. The model feeding with17 frames per second (fps) on real-time video
footage can detect a vehicle with the Mean Squared Error (MSE) of 0.0152, and
the mean license plate character recognition accuracy of 95%. While compared to
the other models, an improvement of 5% and 20% were recorded for the BLPnetover
the prominent YOLO-based ALPR model and the Tesseract model for the
number-plate detection accuracy and time requirement, respectively.

本文提出了一种针对孟加拉语车牌的高效自动车牌识别系统，采用新的端到端深度神经网络模型 Bengali License Plate Network (BLPnet)，实现了字符旋转不变性和实时车牌字符识别，Mean Squared Error 为 0.0152，平均车牌字符识别准确率达到 95%。与其他模型相比，BLPnet 具有更高的号牌检测准确率和更短的时间要求。

BLPnet：一种新的 DNN 模型和孟加拉自动车牌识别 OCR 引擎

BLPnet: A new DNN model and Bengali OCR engine for Automatic License Plate Recognition

Advanced face swapping methods have achieved appealing results. However, most
of these methods have many parameters and computations, which makes it
challenging to apply them in real-time applications or deploy them on edge
devices like mobile phones. In this work, we propose a lightweight
Identity-aware Dynamic Network (IDN) for subject-agnostic face swapping by
dynamically adjusting the model parameters according to the identity
information. In particular, we design an efficient Identity Injection Module
(IIM) by introducing two dynamic neural network techniques, including the
weights prediction and weights modulation. Once the IDN is updated, it can be
applied to swap faces given any target image or video. The presented IDN
contains only 0.50M parameters and needs 0.33G FLOPs per frame, making it
capable for real-time video face swapping on mobile phones. In addition, we
introduce a knowledge distillation-based method for stable training, and a loss
reweighting module is employed to obtain better synthesized results. Finally,
our method achieves comparable results with the teacher models and other
state-of-the-art methods.

提出了一种轻量级的 IDN 人脸交换模型，可用于移动设备上的实时应用，采用动态调整的方法设计了高效的身份注入模块和知识蒸馏方法。在参数量和计算量上得到了大幅优化，可用于任意目标图片和视频的面部交换，并取得了与其他先进方法相当的效果。