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Building autonomous vehicles, robots, and vision AI takes more data than any team can collect on their own. At #CVPR2026#, NVIDIA announced physical AI agent skills to help speed development: composable workflows that automate data generation, simulation, policy training and evaluation, powered by Cosmos 3. Read the blog:

This week at #CVPR2026#, NVIDIA Research is presenting three papers across physical ai that offer groundbreaking solutions for training at scale across diverse applications: → GraspGen-X: the first foundation model for zero-shot grasping, trained on billions of simulated grasps → LCDrive: a model that replaces expensive text-based reasoning with compact latent representations → NitroGen: a generalized gameplay AI foundation model that harnesses NVIDIA Isaac GR00T to help train embodied agents Learn more:

2. CVPR 2026 论文提交 (优先级 5) 规模: 16,092 篇投稿，比 2025 年 增长 24%，接收 4,089 篇（约 25%） 热门方向: - 图像/视频合成与生成（最热） - 视觉、语言与推理 - 多模态学习 - 3D 重建 - 医学/生物视觉（增长最快） NVIDIA NitroGen 模型: - 由 NVIDIA + 斯坦福 + 加州理工 + 芝加哥大学 + 德州大学联合发布 - Vision-action 基础模型，专为通用游戏 Agent 设计 - 在 1,000+ 款游戏、40,000 小时游戏视频上训练 - 定位：跨域通用游戏智能体基础模型 其他亮点: - 深圳先进技术研究院 + vivo 的移动端散景渲染 - 弗吉尼亚大学的扩散模型黑盒攻击 - CMU + 剑桥 + 浙大的无训练肿瘤分割框架