medium
发表于 2025-4-1 02:41:02
,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
非秘密
发表于 2025-4-1 08:39:50
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inchoate
发表于 2025-4-1 13:50:56
,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
赏心悦目
发表于 2025-4-1 16:38:28
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漂亮才会豪华
发表于 2025-4-1 19:23:30
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SCORE
发表于 2025-4-2 00:23:04
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不透明性
发表于 2025-4-2 05:57:46
,: 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