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Generating diverse clothed 3D human animations via a generative model

Computational Visual Media 2024年第2期10卷 261-277页

作者： Min Shi Wenke Feng Lin Gao Dengming Zhu School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Data-driven garment animation is a current topic of interest in the computer graphics *** approaches generally establish the mapping between a single human pose or a temporal pose sequence,and garment deformation,but it is difficult to quickly generate diverse clothed human *** address this problem with a method to automatically synthesize dressed human animations with temporal consistency from a specified human motion *** the heart of our method is a twostage ***,we first learn a latent space encoding the sequence-level distribution of human motions utilizing a transformer-based conditional variational autoencoder(Transformer-CVAE).Then a garment simulator synthesizes dynamic garment shapes using a transformer encoder-decoder *** the learned latent space comes from varied human motions,our method can generate a variety of styles of motions given a specific motion *** means of a novel beginning of sequence(BOS)learning strategy and a self-supervised refinement procedure,our garment simulator is capable of efficiently synthesizing garment deformation sequences corresponding to the generated human motions while maintaining temporal and spatial *** verify our ideas *** is the first generative model that directly dresses human animation.

关键词： Transformer garment animation conditional variational autoencoder(CVAE) computer graphics

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Dynamic Charging and Path Planning for UAV-Powered Rechargeable WSNs Using Multi-Agent Deep Reinforcement Learning

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IEEE Transactions on Automation Science and engineering 2025年 22卷 15610-15626页

作者： Betalo, Mesfin Leranso Leng, Supeng Seid, Abegaz Mohammed Abishu, Hayla Nahom Erbad, Aiman Bai, Xiaoshan Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu611731 China Hamad Bin Khalifa University Division of Information and Computing Technology College of Science and Engineering Doha Qatar Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China

Unmanned Aerial Vehicle (UAV)-powered 5G/6G networks integrated with rechargeable wireless sensor networks (RWSNs) offer promising solutions for extending system lifetime, collecting data, and providing computing services and power to sensor nodes (SNs). UAVs offer significant advantages, including exceptional mobility, cost-effective deployment, and the ability to be easily reprogrammed for a wide range of missions. However, the limited onboard power capacity of UAVs, coupled with the lack of dynamic and intelligent charging station (CS) management and inefficient path planning, can lead to SN failure in dynamic mobile environments. To address these challenges, we propose an energy-efficient laser-charged UAV (LCU)-enabled RWSN environment, wherein UAVs, powered by laser beams from ground-based stations, provide services, collect data, and transfer energy to SNs. We formulate a joint optimization problem involving power allocation, dynamic charging strategy (DCS), and path planning to minimize task completion time and sensor node death time. Given the NP-hard nature of the problem, we employ a stochastic game model based on a Markov decision process (MDP) for its solution. To solve this problem, we propose a deep reinforcement learning (DRL) based algorithm that enables real-time charging scheduling decisions while optimizing network performance. We introduce a multi-agent double deep Q-network (MA-DDQN) model to determine the optimal trajectories for all UAVs in large and complex environments. Simulation results demonstrate that the MA-DDQN approach outperforms state-of-the-art techniques, showing significant improvements in terms of average delay, energy consumption, and task completion time. © 2004-2012 IEEE.

关键词： Stochastic models

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Qualitative Comparison of Vision and Vision-Language Models in Object Detection: Capabilities and Challenges 29

Qualitative Comparison of Vision and Vision-Language Models ...

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29th International Conference on Information Technology, IT 2025

作者： Rakic, Branka Dejanovic, Igor University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Serbia

ISBN: (纸本)9798331517649

Advancements in deep learning and computer vision have greatly enhanced object detection, playing a vital role in applications such as autonomous driving and surveillance. This study explores the trade-offs between two distinct YOLO model architectures: the vision-only models, referred to as pre-YOLO-World, and a vision-language model known as YOLO-World. While the pre-YOLO-World models are renowned for their speed and accuracy, the YOLO-World model integrates large language models to enhance contextual comprehension, enabling open-vocabulary object detection. This paper provides a qualitative analysis of their strengths and limitations, focusing on speed, accuracy, adaptability, computational requirements, and application scenarios. By contrasting these architectures, we aim to offer insights into their potential roles in evolving object detection paradigms and to guide practitioners in selecting models suited to specific use cases. © 2025 IEEE.

关键词： Machine vision

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Indoor Uncertain Semantic Trajectory Similarity Join

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Journal of Computer Science & Technology 2024年第6期39卷 1441-1465页

作者： Hong-Bo Yin Dong-Hua Yang Kai-Qi Zhang Hong Gao Jian-Zhong Li Faculty of Computing Harbin Institute of TechnologyHarbin 150001China Center of Analysis Measurement and ComputingHarbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

With the widespread deployment of indoor positioning systems, an unprecedented scale of indoor trajectories is being produced. By considering the inherent uncertainties and the text information contained in such an indoor trajectory, a new definition named Indoor Uncertain Semantic Trajectory is defined in this paper. In this paper, we focus on a new primitive, yet quite essential query named Indoor Uncertain Semantic Trajectory Similarity Join (IUST-Join for short), which is to match all similar pairs of indoor uncertain semantic trajectories from two sets. IUST-Join targets a number of essential indoor applications. With these applications in mind, we provide a purposeful definition of an indoor uncertain semantic trajectory similarity metric named IUS. To process IUST-Join more efficiently, both an inverted index on indoor uncertain semantic trajectories named 3IST and the first acceleration strategy are proposed to form a filtering-and-verification framework, where most invalid pairs of indoor uncertain semantic trajectories are pruned at quite low computation cost. And based on this filtering-and-verification framework, we present a highly-efficient algorithm named Indoor Uncertain Semantic Trajectory Similarity Join Processing (USP for short). In addition, lots of novel and effective acceleration strategies are proposed and embedded in the USP algorithm. Thanks to these techniques, both the time complexity and the time overhead of the USP algorithm are further reduced. The results of extensive experiments demonstrate the superior performance of the proposed work.

关键词： filtering-and-verification framework indoor uncertain semantic trajectory inverted index trajectory similarity join

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User Input Search - Custom Motion Estimation Algorithm Optimized for UAVs 46

User Input Search - Custom Motion Estimation Algorithm Optim...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Benjak, J. Hofman, D. Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia

ISBN: (纸本)9789532331042

Unmanned aerial vehicle technology is growing rapidly as it finds its application in the various industries, including military & defense, agriculture, logistics, transportation, healthcare, entertainment and many others. One of the fastest growing industries including drones is the entertainment industry. More specifically, First Person View (FPV) piloting has become a popular sport which attracts huge masses. In FPV systems, the pilot wears FPV goggles and controls the drone using a controller, and the drone transmits video data to the goggles in real time. Since drones usually fly very quickly, the video quality in terms of resolution, compression artefacts and end-to-end delay must be maintained. This paper explores the idea of optimizing the Motion Estimation (ME) algorithm of existing High Efficiency Video Coding (HEVC) algorithms by utilizing user input from the controller. For example, if the drone is directed to hover to the left, then it would make sense to search for the most similar blocks in the previous video frame only on the left side of the referent block. For the purpose of this research, a HEVC video coder was customized to receive additional input besides the video data - the user input from the controller, or in other words, drone movement directions. We call this ME algorithm User Input Search (UIS). Our UIS algorithm is compared with the standard ME algorithms and its efficiency is tested. © 2023 MIPRO Croatian Society.

关键词： Real time systems

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Motion-Inspired Real-Time Garment Synthesis with Temporal-Consistency

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Journal of Computer Science & Technology 2023年第6期38卷 1356-1368页

作者：魏育坤石敏冯文科朱登明毛天露 School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Synthesizing garment dynamics according to body motions is a vital technique in computer ***-based simulation depends on an accurate model of the law of kinetics of cloth,which is time-consuming,hard to implement,and complex to *** data-driven approaches either lack temporal consistency,or fail to handle garments that are different from body *** this paper,we present a motion-inspired real-time garment synthesis workflow that enables high-level control of garment *** a sequence of body motions,our workflow is able to gen-erate corresponding garment dynamics with both spatial and temporal *** that end,we develop a transformer-based garment synthesis network to learn the mapping from body motions to garment ***-level attention is employed to capture the dependency of garments and body ***,a post-processing procedure is further tak-en to perform penetration removal and ***,textured clothing animation that is collision-free and tempo-rally-consistent is *** quantitatively and qualitatively evaluated our proposed workflow from different *** experiments demonstrate that our network is able to deliver clothing dynamics which retain the wrinkles from the physics-based simulation,while running 1000 times ***,our workflow achieved superior synthesis perfor-mance compared with alternative *** stimulate further research in this direction,our code will be publicly available soon.

关键词： clothing animation computer graphics transformer temporal consistency

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Accurate Robotic Grasp Detection with Angular Label Smoothing

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Journal of Computer Science & Technology 2023年第5期38卷 1149-1161页

作者：石敏路昊李兆歆朱登明王兆其 School of Control and Computer Engineering North China Electric Power UniversityBeijing 102206China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Grasp detection is a visual recognition task where the robot makes use of its sensors to detect graspable objects in its *** the steady progress in robotic grasping,it is still difficult to achieve both real-time and high accuracy grasping *** this paper,we propose a real-time robotic grasp detection method,which can accurately predict potential grasp for parallel-plate robotic grippers using RGB *** work employs an end-to-end convolutional neural network which consists of a feature descriptor and a grasp *** for the first time,we add an attention mechanism to the grasp detection task,which enables the network to focus on grasp regions rather than ***,we present an angular label smoothing strategy in our grasp detection method to enhance the fault tolerance of the *** quantitatively and qualitatively evaluate our grasp detection method from different aspects on the public Cornell dataset and Jacquard *** experiments demonstrate that our grasp detection method achieves superior performance to the state-of-the-art *** particular,our grasp detection method ranked first on both the Cornell dataset and the Jacquard dataset,giving rise to the accuracy of 98.9%and 95.6%,respectively at realtime calculation speed.

关键词： robotic grasp detection attention mechanism angular label smoothing anchor box deep learning

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Demonstrating the Potential of Visualization in Education with the Manim Python Library: Examples from Algorithms and Data Structures 47

Demonstrating the Potential of Visualization in Education wi...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Marković, Milorad Kaštelan, Ivan University Of Novi Sad Department Of Computing And Control Engineering Sub-department Of Computer Engineering And Communications Novi Sad Serbia

ISBN: (纸本)9798350382495

Visualization is a powerful tool for learning and teaching complex concepts, especially in the field of computer science. However, creating effective and engaging visualizations can be challenging and time-consuming for educators and students. This paper demonstrates how the Manim Python library, a free and open-source project for creating mathematical animations, can be used to create visualizations that illustrate algorithms and data structures in an undergraduate course. The paper showcases several examples of visualizations created with the Manim library, and explains how they help the students to understand the logic, behavior, and performance of the algorithms and data structures. The paper also discusses the benefits and limitations of the Manim library, and suggests some directions for future research and development. © 2024 IEEE.

关键词： Python

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Parks-McClellan Algorithm-Based Active Damping Strategy Applied to Grid-Connected Inverters 8

Parks-McClellan Algorithm-Based Active Damping Strategy Appl...

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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： Filho, José De Arimatéia O. Bressanini, Gabriel Lucas Curi Busarello, Tiago Davi De Oro Arenas, Luis Silva, Paulo Fernando Morales Paredes, Helmo Kelis Sorocaba Brazil Department of Control Automation and Computing Engineering Blumenau Brazil

ISBN: (纸本)9798350321128

In recent years, grid-connected inverters have gained wide prominence, primarily attributed to the surge in distributed energy resources. To ensure the reliable operation of these inverters, it is crucial to adequately damp the resonance peak of the LCL filter at the inverter output. By achieving this, the grid-connected inverter guarantees the system's stability while minimizing the harmonic content in the current injected into the distribution grid. To address this issue, this paper proposes the utilization of digital differentiators in conjunction with optimized wideband differentiators, which are designed using the Parks-McClellan (PM) algorithm. These differentiators are applied specifically to the LCL filter capacitor voltage to dampen the resonance peak effectively. Simulation results univocally demonstrate the effectiveness of the proposed approach. © 2023 IEEE.

关键词： Active damping capacitor voltage digital differentiator LCL-filter Parks-McClellan algorithm

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Person re-identification with transformers and image stacking 32

Person re-identification with transformers and image stackin...

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32nd Telecommunications Forum, TELFOR 2024

作者： Stojanovic, Dimitrije Pavkovic, Bogdan Cetic, Nenad University of Novi Sad Faculty of Technical Sciences Departmant of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798350391053

Person re-identification (ReID) is increasingly important due to the expansion of surveillance cameras. ReID can effectively operate in various conditions, making it suitable for security, retail analytics, and smart city applications. We propose a transformer-based model, DeepChangeVIT-ReID, fine-tuned with triplet loss, using the DeepChange dataset. We address long-term ReID challenges, including pose variations, camera angle differences, and clothing alterations. DeepChangeVIT-ReID achieves state-of-the-art performance, significantly improving Rank-1 accuracy compared to existing methods on the DeepChange dataset. © 2024 IEEE.

关键词： Distribution transformers

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