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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,Generating Physically Realistic and Directable Human Motions from Multi-modal Inputs,ibits the key capabilities of . to out-of-sync input commands, . elements from multiple motion sequences, and . unspecified parts of motions from sparse multimodal input. We demonstrate these key capabilities for an MHC learned over a dataset of 87 diverse skills and showcase different multi-modal u

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,PathMMU: A Massive Multimodal Expert-Level Benchmark for Understanding and Reasoning in Pathology,evel performance benchmark for PathMMU. We conduct extensive evaluations, including zero-shot assessments of 14 open-sourced and 4 closed-sourced LMMs and their robustness to image corruption. We also fine-tune representative LMMs to assess their adaptability to PathMMU. The empirical findings indic

parsimony

,RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios,amples in a gradient-free way, which may originate from templates or tagged scenarios. This in-context learning framework endows versatile generative capabilities, including the ability to edit scenarios, compose various behaviors, and produce critical scenarios. Evaluations show that RealGen offers

gerrymander

,ADen: Adaptive Density Representations for Sparse-View Camera Pose Estimation,re space of rotation uniformly by brute-force. This leads to an inevitable trade-off between high sample density, which improves model precision, and sample efficiency that determines the runtime. In this paper, we propose ADen to unify the two frameworks by employing a generator and a discriminator

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,ViLA: Efficient Video-Language Alignment for Video Question Answering,he state-of-the-art methods on the video question-answering benchmarks: . on STAR Interaction, . on STAR average with . speed up, ours 2-frames out-perform SeViLA 4-frames on the VLEP dataset with . speed-up. Code will be available at

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