Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

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書(shū)目名稱Computer Vision – ECCV 2024影響因子(影響力)




書(shū)目名稱Computer Vision – ECCV 2024影響因子(影響力)學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開(kāi)度




書(shū)目名稱Computer Vision – ECCV 2024網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024被引頻次




書(shū)目名稱Computer Vision – ECCV 2024被引頻次學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024年度引用




書(shū)目名稱Computer Vision – ECCV 2024年度引用學(xué)科排名




書(shū)目名稱Computer Vision – ECCV 2024讀者反饋




書(shū)目名稱Computer Vision – ECCV 2024讀者反饋學(xué)科排名

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,CanonicalFusion: Generating Drivable 3D Human Avatars from?Multiple Images,tegrating individual reconstruction results into the canonical space. To be specific, we first predict Linear Blend Skinning (LBS) weight maps and depth maps using a shared-encoder-dual-decoder network, enabling direct canonicalization of the 3D mesh from the predicted depth maps. Here, instead of p

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,Camera Height Doesn’t Change: Unsupervised Training for?Metric Monocular Road-Scene Depth Estimatioust from regular training data, .., driving videos. We refer to this training framework as FUMET.?The key idea is to leverage cars found on the road as sources of?scale supervision and to incorporate them in network training robustly. FUMET detects and estimates the sizes of cars in a frame?and aggr

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,GENIXER: Empowering Multimodal Large Language Model as?a?Powerful Data Generator,nerate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce ., a comprehensive data generation pipeline consisting of four key steps: (i) instruction data collection, (ii) instruction te

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