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

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,Deblur ,-NeRF: NeRF from Motion-Blurred Events under High-speed or Low-light Conditions,rred events, generated under high-speed or low-light conditions. The core component of this work is a physically-accurate pixel bandwidth model that accounts for event motion blur. We also introduce a threshold-normalized total variation loss to better regularize large textureless patches. Experimen
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,Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual Per are both informative to the current model and persistently challenging throughout active learning. During adaptation, we learn from features of actively selected anchors obtained from previous intermediate models, so that the . can facilitate feature distribution alignment and active sample exploit
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,Motion Mamba: Efficient and Long Sequence Motion Generation,ween frames. We also design a . (.) block to bidirectionally process latent poses, to enhance accurate motion generation within a temporal frame. Our proposed method achieves up to . FID improvement and up to . times faster on the HumanML3D and KIT-ML datasets compared to the previous best diffusion
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