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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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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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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