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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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242306.jpg

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https://doi.org/10.1007/978-3-662-63158-4rceive and understand human emotions, we can significantly improve human-machine interactions. Current research in emotion recognition emphasizes facial expressions, speech and physiological signals, often overlooking body movement’s expressive potential. Existing most methods, reliant on full-body

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https://doi.org/10.1007/978-3-662-63158-4hat generative adversarial networks (GANs) can also benefit from guided sampling, not even requiring to pre-prepare a classifier (.., classifier guidance) or learn an unconditional counterpart (.., classifier-free guidance) as in diffusion models. Inspired by the organized latent space in GANs, we m

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Theoretischer Hintergrund der Untersuchungver, this proficiency remains largely unexplored in other multimodal generative models, particularly in human motion models. By integrating multi-turn conversations in controlling continuous virtual human movements,generative human motion models can achieve an intuitive and step-by-step process of h

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Allelopathic Dynamics in Resource Plantsion recognition, the features obtained from existing pre-trained generative methods contain redundant information unrelated to recognition, which contradicts the nature of the skeleton’s spatially sparse and temporally consistent properties, leading to undesirable performance. To address this challe

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