Recent advances in driving-scene generation and reconstruction have demonstrated significant potential for enhancing autonomous driving systems by producing scalable and controllable training data. Existing generation methods primarily focus on synthesizing diverse and high-fidelity driving videos; however, due to limited 3D consistency and sparse viewpoint coverage, they struggle to support convenient and high-quality novel-view synthesis (NVS). Conversely, recent 3D/4D reconstruction approaches have significantly improved NVS for real-world driving scenes, yet inherently lack generative capabilities. To overcome this dilemma between scene generation and reconstruction, we propose WorldSplat, a novel feed-forward framework for 4D driving-scene generation. Our approach effectively generates consistent multi-track videos through two key steps: (i) We introduce a 4D-aware latent diffusion model integrating multi-modal information to produce pixel-aligned 4D Gaussians in a feed-forward manner. (ii) Subsequently, we refine the novel view videos rendered from these Gaussians using a enhanced video diffusion model. Extensive experiments conducted on benchmark datasets demonstrate that WorldSplat effectively generates high-fidelity, temporally and spatially consistent multi-track novel view driving videos. Project: https://wm-research.github.io/worldsplat/
E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving
McGill University（麦吉尔大学）；University of Macau（澳门大学）；The Hong Kong Polytechnic University（香港理工大学）；Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）
Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene Reconstruction
School of Engineering, Westlake University（西湖大学工学院）；Institute of Advanced Technology, Westlake Institute for Advanced Study（西湖高等研究院先进技术研究院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）
Department of Psychology（心理学系）；Department of Computer Science（计算机科学系）；Stony Brook University（石溪大学）
The visual system processes a scene using a sequence of selective glimpses, each driven by spatial and object-based attention. These glimpses reflect what is relevant to the ongoing task and are selected through recurrent processing and recognition of the objects in the scene. In contrast, most models treat attention selection and recognition as separate stages in a feedforward process. Here we show that using capsule networks to create an object-centric hidden representation in an encoder-decoder model with iterative glimpse attention yields effective integration of attention and recognition.…
UtilGen: Utility-Centric Generative Data Augmentation with Dual-Level Task Adaptation
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；National University of Singapore（新加坡国立大学）；MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China（中国科学技术大学脑科学与智能感知认知教育部重点实验室）；Central South University（中南大学）；Shanghai Jiao Tong University（上海交通大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
