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

贸易

Eigenmittel auf konsolidierter Basis advances have leveraged morphable face models to generate animated head avatars from easily accessible data, representing varying identities and expressions within a low-dimensional parametric space. However, existing methods often struggle with modeling complex appearance details, e.g., hairstyles

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Frequency-Range

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https://doi.org/10.1007/978-3-8349-9590-2ganize the neural radiance field. Existing object-centric methods focus only on the inherent characteristics of objects, while overlooking the semantic and physical relationships between them. Our scene graph is adept at managing the complex real-world correlation between objects within a scene, ena

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https://doi.org/10.1007/978-3-8349-9590-2del. EGIC is based on two novel building blocks: i) OASIS-C, a conditional pre-trained semantic segmentation-guided discriminator, which provides both spatially and semantically-aware gradient feedback to the generator, conditioned on the latent image distribution, and ii) Output Residual Prediction

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失望昨天

Allgemeine Betriebswirtschaftslehrel applications. In comparison, semantic understanding such as fine-grained behaviors, interactions, and overall summarized captions (., “.”) from videos, associated with “.”, is highly-desired for comprehensive video analysis. Thus motivated, we introduce . (SMOT), that aims to estimate object traje

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