Computer Vision and Image Understanding最新文献

{"title":"3D-aware virtual try-on using only 2D inputs","authors":"Jaeyoon Lee ,&nbsp;Hojoon Jung ,&nbsp;Jongwon Choi","doi":"10.1016/j.cviu.2026.104661","DOIUrl":"10.1016/j.cviu.2026.104661","url":null,"abstract":"<div><div>We present 3DFit, which is a novel 3D-aware virtual try-on framework that synthesizes realistic try-on images using only 2D inputs. Unlike previous methods that either ignore 3D body geometry or rely entirely on 3D clothing models, 3DFit utilizes 3D human meshes estimated from 2D images and adaptively transforms 3D clothing templates guided by 2D clothing images. We further introduce a warping strategy that integrates 3D information into 2D clothing images using a set of pre-designed 3D templates, enabling efficient adaptation to various body shapes and poses. As a result, our method supports accurate and personalized virtual try-on experiences. Experimental results on the VITON-HD dataset demonstrate that 3DFit outperforms existing methods in preserving garment structure and maintaining high visual quality across a wide range of body types and poses.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104661"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interaction-aware representation learning for action quality assessment in freestyle skiing big air","authors":"Shiyue Chen ,&nbsp;Yanchao Liu ,&nbsp;Ziyue Wang ,&nbsp;Xina Cheng ,&nbsp;Takeshi Ikenaga","doi":"10.1016/j.cviu.2026.104634","DOIUrl":"10.1016/j.cviu.2026.104634","url":null,"abstract":"<div><div>Freestyle skiing big air requires precise athlete–ski coordination to determine both technical difficulty and execution quality. Accurate action quality assessment in this discipline therefore necessitates explicit modeling of human–object interactions. However, most existing methods rely on video-level or human-centric representations, overlooking structured athlete-ski relationships and limiting evaluation of control and stability. To address this, we construct a freestyle skiing big air dataset with fine-grained annotations, including frame-level athlete-ski bounding boxes and performance-related metadata. Based on this dataset, we propose an interaction-aware framework that captures athlete–ski coordination by combining instance-level appearance and positional features through spatiotemporal reasoning. Furthermore, to avoid commonly used uniform sampling diluting performance-critical moments in long sequences, we introduce a training-free entropy-based sampling strategy that exploits athlete–ski geometric dynamics to identify performance-critical moments such as take-off, rotation, and landing, thereby reducing redundancy. Together, these designs address <em>where to look</em> and <em>when to focus</em> in big air assessment. Extensive experiments demonstrate that our method achieves a Spearman’s rank correlation of 0.7173 on the proposed dataset, outperforming state-of-the-art methods.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104634"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EventSleep2: Sleep activity recognition on complete night sleep recordings with an event camera","authors":"Nerea Gallego ,&nbsp;Carlos Plou ,&nbsp;Miguel Marcos ,&nbsp;Pablo Urcola ,&nbsp;Luis Montesano ,&nbsp;Eduardo Montijano ,&nbsp;Ruben Martinez-Cantin ,&nbsp;Ana C. Murillo","doi":"10.1016/j.cviu.2025.104619","DOIUrl":"10.1016/j.cviu.2025.104619","url":null,"abstract":"<div><div>Sleep is fundamental to health, and society is more and more aware of the impact and relevance of sleep disorders. Traditional diagnostic methods, like polysomnography, are intrusive and resource-intensive. Instead, research is focusing on developing novel, less intrusive or portable methods that combine intelligent sensors with activity recognition for diagnosis support and scoring. Event cameras offer a promising alternative for automated, in-home sleep activity recognition due to their excellent low-light performance and low power consumption. This work introduces <strong>EventSleep2-data</strong>, a significant extension to the EventSleep dataset, featuring 10 complete night recordings (around 7 h each) of volunteers sleeping in their homes. Unlike the original short and controlled recordings, this new dataset captures natural, full-night sleep sessions under realistic conditions. This new data incorporates challenging real-world scene variations, an efficient movement-triggered sparse data recording pipeline, and synchronized 2-channel EEG data for a subset of recordings. We also present <strong>EventSleep2-net</strong>, a novel event-based sleep activity recognition approach with a dual-head architecture to simultaneously analyze motion classes and static poses. The model is specifically designed to handle the motion-triggered, sparse nature of complete night recordings. Unlike the original EventSleep architecture, EventSleep2-net can predict both movement and static poses even during long periods with no events. We demonstrate state-of-the-art performance on both EventSleep1-data, the original dataset, and EventSleep2-data, with comprehensive ablation studies validating our design decisions. Together, EventSleep2-data and EventSleep2-net overcome the limitations of the previous setup and enable continuous, full-night analysis for real-world sleep monitoring, significantly advancing the potential of event-based vision for sleep disorder studies. Code and data are publicly available on the webpage: <span><span>https://sites.google.com/unizar.es/eventsleep</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104619"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dual-channel model based on multi-feature fusion for face liveness detection","authors":"Bowen Xu ,&nbsp;Yaru Sui ,&nbsp;Longxin Liu,&nbsp;Zhenlong Ma,&nbsp;Yunlong Shi,&nbsp;Wentong Li,&nbsp;Xiaoqiang Ji","doi":"10.1016/j.cviu.2026.104635","DOIUrl":"10.1016/j.cviu.2026.104635","url":null,"abstract":"<div><div>Face liveness detection algorithms are widely used in anti-spoofing applications, which guarantee the accuracy and security of face recognition systems. However, with the continuous development of technologies such as 3D printing and artificial intelligence, traditional face-liveness detection algorithms struggle to withstand spoofing attacks effectively. In this paper, we propose a multi-feature fusion algorithm using only facial video for face liveness detection. Initially, we design a dual-channel network named DC-Net. It can extract robust remote photoplethysmography signals directly from 5-second facial videos, as well as fine global texture features from the keyframes of the image sequence. Subsequently, a fusion module based on the attention mechanism is used to carry out feature-level fusion. Ultimately, we use the fully connected layer for binary classification. Our methodology was validated using the REPLAY-ATTACK dataset and the 3DMAD dataset, demonstrating that for printing attacks, screen replay attacks, and 3D mask attacks, our approach attained an accuracy of 99.79% and 100% on both datasets, respectively. Meanwhile, cross-dataset testing was conducted on the CASIA-FASD and HKBU-MARs V1+ datasets, achieving HTER of 25.56% and 0.00%, respectively. This indicates that the algorithm has good accuracy and robustness in dealing with spoofing attacks in many different scenarios, which provides important ideas and technical support for the design and implementation of reliable face recognition systems.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104635"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145928064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A YOLO-OC real-time small object detection in ocean scenes","authors":"Yang Zhang ,&nbsp;Tao Qin ,&nbsp;Yimin Zhou","doi":"10.1016/j.cviu.2025.104625","DOIUrl":"10.1016/j.cviu.2025.104625","url":null,"abstract":"<div><div>The ocean scenes are usually intricate and complex, with low signal-to-noise ratios for tiny or distant objects and susceptible to interference from underwater background and lighting conditions, which makes the general object detection methods more difficult to be directly applied in the ocean scenes. To solve the above problems, a YOLO-OCEAN method is proposed the YOLOv5 as the baseline model. An ultra-small-scale feature layer, multi-branch feature enhancement with cross-scale fusion, a visual-transformer bridge, a CSP-connected SPPF block and dynamic activation are incorporated into the backbone and neck to improve the detection performance. More Efficient Intersection over Union regression loss function is applied to the detection head structure. Moreover, the model is re-parameterized and lightweighted to enhance the detection speed of the model. Comparison experiments have been performed with other object detection baseline models where 86.6% [email protected] and 5.1 ms inference time are achieved with the proposed YOLO-OC method, proving the real-time detection capability for small objects in ocean scenes with high accuracy.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104625"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978917","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Indoor UAV navigation using event cameras and intermediate frame reconstruction","authors":"David Tejero-Ruiz ,&nbsp;David Solís-Martín ,&nbsp;Francisco J. Pérez-Grau ,&nbsp;Joaquín Borrego-Díaz","doi":"10.1016/j.cviu.2026.104650","DOIUrl":"10.1016/j.cviu.2026.104650","url":null,"abstract":"<div><div>Indoor UAV navigation faces significant challenges due to GPS signal absence and limitations of conventional visual-inertial systems under challenging lighting and motion conditions. This paper presents an event-based visual-inertial odometry system that addresses these limitations through intermediate frame reconstruction from event streams combined with established odometry algorithms. The approach leverages event cameras’ unique characteristics — microsecond temporal resolution, high dynamic range (120 dB), and motion blur immunity — to maintain stable navigation performance under conditions that cause conventional systems to fail. The system achieves real-time operation at 30 Hz frame reconstruction and 20 Hz pose estimation on embedded hardware, consuming 15 W power while adding only 50 g to the UAV platform. Experimental validation in controlled indoor environments demonstrates mean absolute pose errors of 26–42 cm across different operational conditions, comparable to conventional visual-inertial systems. Critically, the system maintains stable performance during rapid lighting transitions, showing only 59% performance degradation compared to baseline conditions, while conventional cameras typically experience complete tracking failure. The results establish event-based visual-inertial odometry as a viable alternative for indoor UAV navigation, particularly in applications requiring environmental robustness over marginal accuracy improvements under optimal conditions.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104650"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extending Large Language Models to multimodality for non-English languages","authors":"Elio Musacchio ,&nbsp;Lucia Siciliani ,&nbsp;Pierpaolo Basile ,&nbsp;Giovanni Semeraro","doi":"10.1016/j.cviu.2025.104618","DOIUrl":"10.1016/j.cviu.2025.104618","url":null,"abstract":"<div><div>The growing popularity of Large Vision-Language Models has highlighted and intensified one of the most well-known challenges in the field of Large Language Models: training is mainly, and most of the time exclusively, conducted on English data. Consequently, the resulting models are more prone to error in non-English tasks, and this issue is exacerbated in multimodal settings that are even more complex and use task-specific datasets. Given this, research on Large Language Models has turned toward adapting them to non-English languages. However, the scarcity of open and curated resources for these languages poses a significant limitation. In this work, we aim to tackle the aforementioned challenge by exploring Large Vision-Language Models adaptation to non-English languages, using machine translation to overcome the lack of curated data. We also analyze how the evaluation of the results is influenced when training a vision-to-text adapter across different languages, examining the performance variations and challenges associated with multilingual adaptation. Finally, we highlight the importance of using open resources to ensure transparency and reproducibility of the results. Following this philosophy, we provide open access to the entire codebase of the adaptation pipeline, along with the trained models and dataset, to foster further research.<span><span>¹</span></span></div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"264 ","pages":"Article 104618"},"PeriodicalIF":3.5,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Question-guided multigranular visual augmentation for knowledge-based visual question answering","authors":"Jing Liu ,&nbsp;Lizong Zhang ,&nbsp;Chong Mu ,&nbsp;Guangxi Lu ,&nbsp;Ben Zhang ,&nbsp;Junsong Li","doi":"10.1016/j.cviu.2025.104569","DOIUrl":"10.1016/j.cviu.2025.104569","url":null,"abstract":"<div><div>In knowledge-based visual question answering, most current research focuses on the integration of external knowledge with VQA systems. However, the extraction of visual features within knowledge-based VQA remains relatively unexplored. This is surprising since even for the same image, answering different questions requires attention to different visual regions. In this paper, we propose a novel question-guided multigranular visual augmentation method for knowledge-based VQA tasks. Our method uses input questions to identify and focus on question-related regions within the image, which improves prediction quality. Specifically, our method first performs semantic embedding learning for questions at both the word-level and the phrase-level. To preserve rich visual information for QA, our method uses questions as a guide to extract question-related visual features. This is implemented by multiple convolution operations. In these operations, the convolutional kernels are dynamically derived from the representations of questions. By capturing visual information from diverse perspectives, our method extract information at the word level, phrase level, and common level more comprehensively. Additionally, relevant knowledge is retrieved from knowledge graph through entity linking and random walk techniques to respond to the question. A series of experiments are conducted on public knowledge-based VQA datasets to demonstrate the effectiveness of our model. The experimental results show that our method achieves state-of-the-art performance.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"263 ","pages":"Article 104569"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618395","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SynTaskNet: A synergistic multi-task network for joint segmentation and classification of small anatomical structures in ultrasound imaging","authors":"Abdulrhman H. Al-Jebrni ,&nbsp;Saba Ghazanfar Ali ,&nbsp;Bin Sheng ,&nbsp;Huating Li ,&nbsp;Xiao Lin ,&nbsp;Ping Li ,&nbsp;Younhyun Jung ,&nbsp;Jinman Kim ,&nbsp;Li Xu ,&nbsp;Lixin Jiang ,&nbsp;Jing Du","doi":"10.1016/j.cviu.2025.104616","DOIUrl":"10.1016/j.cviu.2025.104616","url":null,"abstract":"<div><div>Segmenting small, low-contrast anatomical structures and classifying their pathological status in ultrasound (US) images remain challenging tasks in computer vision, especially under the noise and ambiguity inherent in real-world clinical data. Papillary thyroid microcarcinoma (PTMC), characterized by nodules <span><math><mrow><mo>≤</mo><mn>1</mn><mo>.</mo><mn>0</mn></mrow></math></span> cm, exemplifies these challenges where both precise segmentation and accurate lymph node metastasis (LNM) prediction are essential for informed clinical decisions. We propose SynTaskNet, a synergistic multi-task learning (MTL) architecture that jointly performs PTMC nodule segmentation and LNM classification from US images. Built upon a DenseNet201 backbone, SynTaskNet incorporates several specialized modules: a Coordinated Depth-wise Convolution (CDC) layer for enhancing spatial features, an Adaptive Context Block (ACB) for embedding contextual dependencies, and a Multi-scale Contextual Boundary Attention (MCBA) module to improve boundary localization in low-contrast regions. To strengthen task interaction, we introduce a Selective Enhancement Fusion (SEF) mechanism that hierarchically integrates features across three semantic levels, enabling effective information exchange between segmentation and classification branches. On top of this, we formulate a synergistic learning scheme wherein an Auxiliary Segmentation Map (ASM) generated by the segmentation decoder is injected into SEF’s third class-specific fusion path to guide LNM classification. In parallel, the predicted LNM label is concatenated with the third-path SEF output to refine the Final Segmentation Map (FSM), enabling bidirectional task reinforcement. Extensive evaluations on a dedicated PTMC US dataset demonstrate that SynTaskNet achieves state-of-the-art performance, with a Dice score of 93.0% for segmentation and a classification accuracy of 94.2% for LNM prediction, validating its clinical relevance and technical efficacy.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"263 ","pages":"Article 104616"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145790196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A vision-based framework and dataset for human behavior understanding in industrial assembly lines","authors":"Konstantinos Papoutsakis ,&nbsp;Nikolaos Bakalos ,&nbsp;Athena Zacharia ,&nbsp;Konstantinos Fragkoulis ,&nbsp;Georgia Kapetadimitri ,&nbsp;Maria Pateraki","doi":"10.1016/j.cviu.2025.104592","DOIUrl":"10.1016/j.cviu.2025.104592","url":null,"abstract":"<div><div>This paper introduces a vision-based framework and dataset for capturing and understanding human behavior in industrial assembly lines, focusing on car door manufacturing. The framework leverages advanced computer vision techniques to estimate workers’ locations and 3D poses and analyze work postures, actions, and task progress. A key contribution is the introduction of the CarDA dataset, which contains domain-relevant assembly actions captured in a realistic setting to support the analysis of the framework for human pose and action analysis. The dataset comprises time-synchronized multi-camera RGB-D videos, motion capture data recorded in a real car manufacturing environment, and annotations for EAWS-based ergonomic risk scores and assembly activities. Experimental results demonstrate the effectiveness of the proposed approach in classifying worker postures and robust performance in monitoring assembly task progress.</div></div>","PeriodicalId":50633,"journal":{"name":"Computer Vision and Image Understanding","volume":"263 ","pages":"Article 104592"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}