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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,LEGO: ,earning ,centric Action Frame Generation via Visual Instruction Tuning,anguage model (VLLM) by visual instruction tuning. Then we propose a novel method to leverage image and text embeddings from the VLLM as additional conditioning to improve the performance of a diffusion model. We validate our model on two egocentric datasets – Ego4D and Epic-Kitchens. Our experiment

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,Mesh2NeRF: Direct Mesh Supervision for Neural Radiance Field Representation and Generation,f Mesh2NeRF across various tasks, achieving a noteworthy 3.12dB improvement in PSNR for view synthesis in single scene representation on the ABO dataset, a 0.69 PSNR enhancement in the single-view conditional generation of ShapeNet Cars, and notably improved mesh extraction from NeRF in the uncondit

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,: Scaling 3D Vision-Language Learning for Grounded Scene Understanding,aph-based generation approach. We demonstrate that this scaling allows for a unified pre-training framework, Grounded Pre-training for Scenes (.), for 3D-VL learning. Through extensive experiments, we showcase the effectiveness of . by achieving state-of-the-art performance on existing 3D visual gro