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Titlebook: Computational Visual Media; 12th International C Fang-Lue Zhang,Andrei Sharf Conference proceedings 2024 The Editor(s) (if applicable) and

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发表于 2025-3-21 19:15:18 | 显示全部楼层 |阅读模式
书目名称Computational Visual Media
副标题12th International C
编辑Fang-Lue Zhang,Andrei Sharf
视频videohttp://file.papertrans.cn/234/233216/233216.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Visual Media; 12th International C Fang-Lue Zhang,Andrei Sharf Conference proceedings 2024 The Editor(s) (if applicable) and
描述This book constitutes the refereed proceedings of CVM 2024, the 12th International Conference on Computational Visual Media, held in Wellington, New Zealand, in April 2024..The 34 full papers were carefully reviewed and selected from 212 submissions. The papers are organized in topical sections as follows:.Part I: Reconstruction and Modelling, Point Cloud, Rendering and Animation, User Interations..Part II: Facial Images, Image Generation and Enhancement, Image Understanding, Stylization, Vision Meets Graphics..
出版日期Conference proceedings 2024
关键词Animation and physical simulation; Cognition of visual media; Content security of visual media; Editing
版次1
doihttps://doi.org/10.1007/978-981-97-2092-7
isbn_softcover978-981-97-2091-0
isbn_ebook978-981-97-2092-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

书目名称Computational Visual Media影响因子(影响力)




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书目名称Computational Visual Media网络公开度




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Explore and Enhance the Generalization of Anomaly DeepFake Detection These detection methods primarily enhance generalization by constructing pseudo-fake samples, which involve three main steps: mask generation, source-target preprocessing, and blending. In this paper, we conducted a systematic analysis of some core factors in these steps. Based on the aforementione
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Face Expression Recognition via Product-Cross Dual Attention and Neutral-Aware Anchor Losshis task is challenging due to the ambiguities in expressions and also in the diverse poses and occlusions of the head. To handle this challenging task, recent approaches usually rely on attention mechanism to make the network focus on the most critical regions of a face, or apply a consistency loss
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Single-Video Temporal Consistency Enhancement with Rolling Guidancelic. However, ensuring the temporal consistency of generated videos is still a challenging problem. Most existing algorithms for temporal consistency enhancement rely on the motion cues from a guidance video to filter the temporally inconsistent video. This paper proposes a novel approach that proce
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Silhouette-Based 6D Object Pose Estimationwn objects beyond the training datasets, due to the closed-set assumption and the expensive cost of high-quality annotation. Conversely, traditional methods struggle to achieve accurate pose estimation for texture-less objects. In this work, we propose a silhouette-based 6D object pose estimation me
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Robust Light Field Depth Estimation over Occluded and Specular Regionsh range, with the highest level of consistency indicating the correct depth. These methods are based on the photo consistency of Lambertian surface. However, the photo consistency is broken when occlusion and specular reflection occur. In this paper, a new depth estimation algorithm is proposed to s
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Foreground and Background Separate Adaptive Equilibrium Gradients Loss for Long-Tail Object Detectioss. However, in the presence of long-tail distribution, the performance is still unsatisfactory. Long-tail data distribution means that a few head classes occupy most of the data, while most of the tail classes are not representative, and tail classes are excessive negatively suppressed during train
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