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Motion Editing for Quadruped Characters via Latent Frequency Embedding

IEEE Transactions on Visualization and computer Graphics 2024年 PP卷 PP页

作者： Zeng, Rui Pan, Junjun Dai, Ju Gao, Yang Bai, Junxuan Qin, Hong Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing China Pengcheng Laboratory Shenzhen China Capital University of Physical Education and Sports Beijing China Beijing China Department of Computer Science Stony BrookNY11794-2424 United States

The accurate and diversified generation of motion sequences for virtual characters poses both an enticing and challenging task within the domain of 3D animation and game content production. To achieve a natural and realistic full-body motion, the movements of virtual characters must adhere to a set of constraints, promoting reliable and seamless pose-changing. This study presents a two-stage model specifically designed to learn Inverse Kinematics (IK) constraints from the representative quadruped character poses. In the first stage, we employ frequency analysis to decompose motion poses into the base-level and style-level components. The base-level content encapsulates the global correlations in the dataset, while the style-level variation centers on distinguishing the local attributes in similar data elements. In order to construct data correlations among poses, we embed the decomposed pose feature into a latent space in the second stage. The kernel matrix of the embedding, which is refined from the original joint angles to the decomposed representation and the IK constraints, creates a more compact distribution of the pose similarity and also guarantees a plausible sampling result with certain IK constraints. Moreover, new motions from the edited IK constraints can also be generated by proposing a searching strategy to adapt to our latent embedding. Experimental results reveal that our method is competitive with the state-of-the-art synthetic approaches in terms of accuracy, highlighting our considerable potential for high efficiency in the animation production. © 1995-2012 IEEE.

关键词： Inverse kinematics

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Simulating the Dilution of Sulfuric Acid on Mixed reality Platform 8

Simulating the Dilution of Sulfuric Acid on Mixed Reality Pl...

引用

8th International Conference on virtual reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

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HOIAnimator: Generating Text-Prompt Human-Object Animations Using Novel Perceptive Diffusion Models

HOIAnimator: Generating Text-Prompt Human-Object Animations ...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Wenfeng Song Xinyu Zhang Shuai Li Yang Gao Aimin Hao Xia Hau Chenglizhao Chen Ning Li Hong Qin Beijing Information Science and Technology University Zhongguancun Laboratory China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Research Unit of Virtual Human and Virtual Surgery (2019RU004) Chinese Academy of Medical Sciences College of Computer Science and Technology China University of Petroleum (East China) Department of Computer Science Stony Brook University (SUNY at Stony Brook) New York USA

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

To date, the quest to rapidly and effectively produce human-object interaction (HOI) animations directly from textual descriptions stands at the forefront of computer vision research. The underlying challenge demands both a discriminating interpretation of language and a comprehen-sive physics-centric model supporting real-world dynamics. To ameliorate, this paper advocates HOIAnimator, a novel and interactive diffusion model with perception ability and also ingeniously crafted to revolutionize the animation of complex interactions from linguistic narratives. The effectiveness of our model is anchored in two ground-breaking innovations: (1) Our Perceptive Diffusion Models (PDM) brings together two types of models: one focused on hu-man movements and the other on objects. This combination allows for animations where humans and objects move in concert with each other, making the overall motion more realistic. Additionally, we propose a Perceptive Message Passing (PMP) mechanism to enhance the communication bridging the two models, ensuring that the animations are smooth and unified; (2) We devise an Interaction Contact Field (ICF), a sophisticated model that implicitly captures the essence of HOls. Beyond mere predictive contact points, the ICF assesses the proximity of human and object to their respective environment, informed by a probabilistic distribution of interactions learned throughout the denoising phase. Our comprehensive evaluation showcases HOlani-mator's superior ability to produce dynamic, context-aware animations that surpass existing benchmarks in text-driven animation synthesis.

关键词： computer vision Technological innovation Message passing Computational modeling Noise reduction Linguistics Animation

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PACF: Prototype Augmented Compact Features for Improving Domain Adaptive Object Detection

arXiv

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arXiv 2025年

作者： Liu, Chenguang Feng, Yongchao Zhang, Yanan Liu, Qingjie Wang, Yunhong State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China Hangzhou Innovation Institute Beihang University Hangzhou310051 China

In recent years, there has been significant advancement in object detection. However, applying off-the-shelf detectors to a new domain leads to significant performance drop, caused by the domain gap. These detectors exhibit higher-variance class-conditional distributions in the target domain than that in the source domain, along with mean shift. To address this problem, we propose the Prototype Augmented Compact Features (PACF) framework to regularize the distribution of intra-class features. Specifically, we provide an in-depth theoretical analysis on the lower bound of the target features-related likelihood and derive the prototype cross entropy loss to further calibrate the distribution of target RoI features. Furthermore, a mutual regularization strategy is designed to enable the linear and prototype-based classifiers to learn from each other, promoting feature compactness while enhancing discriminability. Thanks to this PACF framework, we have obtained a more compact cross-domain feature space, within which the variance of the target features’ class-conditional distributions has significantly decreased, and the class-mean shift between the two domains has also been further reduced. The results on different adaptation settings are state-of-the-art, which demonstrate the board applicability and effectiveness of the proposed approach. Copyright © 2025, The Authors. All rights reserved.

关键词： Object detection

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Bacterial population dynamics and biosafety assessment in a closed Bioregenerative Life Support System during the “Lunar Palace 365” experiment

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Science China Life Sciences 2025年第7期68卷 2137-2149页

作者： Yuming Fu Yueying Lu Jianlou Yang Hong Liu Key Laboratory of Biomechanics and Mechanobiology (Beihang University) Ministry of Education Key Laboratory of Innovation and Transformation of Advanced Medical Devices Ministry of Industry and Information Technology National Medical Innovation Platform for Industry-Education Integration in Advanced Medical Devices (Inte rdiscipline of Medicine and Engineering) School of Biological Science and Medical Engineering Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University International Joint Research Center of Aerospace Biotechnology & Medical Engineering Beihang University School of Sport Management Shan Dong Sport University Innovation Center for Medical Engineering & Engineering Medicine Hangzhou International Innovation Institute Beihang University

Our study investigates the dynamic variation of bacterial populations within the Bioregenerative Life Support System(BLSS), a critical component for sustainable habitats in extraterrestrial environments supporting human survival. We utilized the ground-based BLSS test platform, referred to as “Lunar Palace 1”(LP1), to examine bacterial dynamics in an ecosystem integrating plant production, human cultivation, and equipment maintenance. Our findings reveal distinct bacterial compositions among crew members and functional areas within the LP1 system while preserving temporal stability across the 370-day experiment. Importantly, we observed a low abundance of potential pathogens, minimal antibiotic resistance, and negligible impact of the isolated bacterial strains on equipment materials, suggesting favorable biosafety attributed to plant integration. Our work enhances the understanding of microbial communities in BLSS and underscores the importance of incorporating green plants to bolster environmental biosafety and sustainability in long-term space exploration missions.

关键词： bacterial community dynamics Bioregenerative Life Support System biosafety antibiotic resistance genes biodegradation

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Exploring the Factors of Students’ Online Learning Based On Structural Equation Modeling

Exploring the Factors of Students’ Online Learning Based On...

引用

IEEE International Conference on Teaching, Assessment and Learning for Engineering (TALE)

作者： Han Wan Hongzhen Luo Zihao Zhong Jianlei Yang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

Due to the COVID-19 pandemic, traditional teaching has been migrated online. Different from traditional face-to-face teaching, when students learn online, the online learning platform will generate various data. And these data make it possible for us to analyze students’ final academic performance. In this paper, we use structural equation modeling (SEM) to analyze the relationship between students’ learning factors. It is found that students’ lab scores (LS), exercise scores (LS) and participation in the discussion (PID) have a direct impact on their final programming scores (FPS). This paper also finds that students’ assignment submissions have an indirect influence on their final programming scores (FPS) but have a direct effect on lab scores (LS). In addition, students’ participation in the discussion (PID) has an indirect influence on assignment submissions (AS) and lab scores (LS). The research in this paper can provide instructional designers with references for instructional design.

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Radiographic Reports Generation via Retrieval Enhanced Cross-modal Fusion

Radiographic Reports Generation via Retrieval Enhanced Cross...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xia Hou Yifan Luo Wenfeng Song Yuting Guo Wenzhe You Shuai Li School of Computer Science Beijing Information Science and Technology University Beijing China The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Stomatology Center of China-Japan Friendship Hospital Beijing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Accurate radiographic reports are crucial for effective clinical decision-making and patient safety, as they directly influence diagnosis and treatment plans. Existing models for radiographic report generation often struggle with integrating medical image and textual report features and addressing data imbalances. To overcome these limitations, we propose an enhanced cross-modal aligned retrieval-driven network (EARnet). Our model incorporates two key innovations: Enhanced Cross-modal Alignment (ECA) module and Case-based Retrieval Augmenter (CRA) module. ECA ensures the effective integration of medical visual and textual data by aligning these modalities. CRA helps mitigate data imbalance by improving the representation of medical information and ensuring a more balanced and comprehensive coverage of both normal and abnormal cases. This dual-module approach significantly improves the coherence and accuracy of the generated medical reports. Evaluation results demonstrate that our EARnet model substantially outperforms existing methods in terms of report quality and accuracy across multiple metrics. Our codes and models are available at https://***/lyf616/EARnet.

关键词： Measurement Visualization Technological innovation Accuracy Biological system modeling Decision making Data models Safety Diagnostic radiography Medical diagnostic imaging

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Spatiotemporal consistency-based adaptive hand-held video stabilization

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Science China(Information Sciences) 2020年第1期63卷 226-228页

作者： Xiao LI Shuai LI Hong QIN Aimin HAO State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beihang University Qingdao Research Institute Department of Computer Science Stony Brook University

This study develops a novel, joint optimization method to successively respect spatial structure consistency and temporal feature constraints. Our method is more flexible and adaptive as we regard the camera multitrack motion to be noisy signals and adaptively suppress the noise without manual intervention. In addition, our method can achieve stabilization effects similar to that obtained by 3D

关键词： IMF EMD red frame Spatiotemporal consistency-based adaptive hand-held video stabilization

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Language-Inspired Relation Transfer for Few-shot Class-Incremental Learning

arXiv

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arXiv 2025年

作者： Zhao, Yifan Li, Jia Song, Zeyin Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China School of Electronic and Computer Engineering Peking University Shenzhen518055 China School of Computer Science Peking University Beijing100871 China Pengcheng Laboratory Shenzhen518055 China

Depicting novel classes with language descriptions by observing few-shot samples is inherent in human-learning systems. This lifelong learning capability helps to distinguish new knowledge from old ones through the increase of open-world learning, namely Few-Shot Class-Incremental Learning (FSCIL). Existing works to solve this problem mainly rely on the careful tuning of visual encoders, which shows an evident trade-off between the base knowledge and incremental ones. Motivated by human learning systems, we propose a new Language-inspired Relation Transfer (LRT) paradigm to understand objects by joint visual clues and text depictions, composed of two major steps. We first transfer the pretrained text knowledge to the visual domains by proposing a graph relation transformation module and then fuse the visual and language embedding by a text-vision prototypical fusion module. Second, to mitigate the domain gap caused by visual finetuning, we propose context prompt learning for fast domain alignment and imagined contrastive learning to alleviate the insufficient text data during alignment. With collaborative learning of domain alignments and text-image transfer, our proposed LRT outperforms the state-of-the-art models by over 13% and 7% on the final session of miniImageNet and CIFAR-100 FSCIL benchmarks. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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Generative Cooperative Network for Person Image Generation

Generative Cooperative Network for Person Image Generation

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International Conference on Universal Village (UV)

作者： Yang Liu Shuai Wang Yubin Wu Hao Sheng Zhang Xiong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China

Person image generation is to synthesize realistic pedestrian images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: some methods use human pose information as guidance and others try to generate person images from scratch. The former is to transfer the pose of a source image to a reference pose. The generated person image have the same identity as the source image. The latter takes a random noise from latent space as input, and the real person images are only used as references for the discriminator. While pose-guided person image generation is widely studied, generating-from-scratch methods are also worth exploring because they can synthesize person image with new identity, which is a useful manner of data augmentation. These two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. In this work, the authors design a Generative Cooperative Network (GCN) to jointly train two types of GANs. The two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. The proposed approach is verified on public datasets, and the results show that our GCN improves the performance of the baseline methods. Comparisons with state-of-the-art methods also prove the effectiveness of the proposed method.

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