4 citations · 11 across the 13 of their papers we have counts for
14 papers
AnyScene: Towards Highly Controllable Driving Scene Generation at Anywhere and Beyond
Haiming Zhang, Junfei Zhou, Feng Jiang +6
Generating high-fidelity and controllable synthetic data is critical for advancing end-to-end autonomous driving, particularly for addressing the long tail of rare safety-critical…
MindVLA-U1: VLA Beats VA with Unified Streaming Architecture for Autonomous Driving
Yuzhou Huang, Benjin Zhu, Hengtong Lu +6
Autonomous driving has progressed from modular pipelines toward end-to-end unification, and Vision-Language-Action (VLA) models are a natural extension of this journey beyond Visio…
DLWM: Dual Latent World Models enable Holistic Gaussian-centric Pre-training in Autonomous Driving
Yiyao Zhu, Ying Xue, Haiming Zhang +8
Vision-based autonomous driving has gained much attention due to its low costs and excellent performance. Compared with dense BEV (Bird's Eye View) or sparse query models, Gaussian…
From Words to Safety: Language-Conditioned Safety Filtering for Robot Navigation
Zeyuan Feng, Haimingyue Zhang, Somil Bansal
As robots become increasingly integrated into open-world, human-centered environments, their ability to interpret natural language instructions and adhere to safety constraints is…
SQS: Enhancing Sparse Perception Models via Query-based Splatting in Autonomous Driving
Haiming Zhang, Yiyao Zhu, Wending Zhou +5
Sparse Perception Models (SPMs) adopt a query-driven paradigm that forgoes explicit dense BEV or volumetric construction, enabling highly efficient computation and accelerated infe…
An Efficient Occupancy World Model via Decoupled Dynamic Flow and Image-assisted Training
Haiming Zhang, Ying Xue, Xu Yan +6
The field of autonomous driving is experiencing a surge of interest in world models, which aim to predict potential future scenarios based on historical observations. In this paper…
D-World: An Efficient World Model through Decoupled Dynamic Flow
Haiming Zhang, Xu Yan, Ying Xue +4
This technical report summarizes the second-place solution for the Predictive World Model Challenge held at the CVPR-2024 Workshop on Foundation Models for Autonomous Systems. We i…
VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving
Haiming Zhang, Wending Zhou, Yiyao Zhu +7
This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that…
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
Xu Yan, Haiming Zhang, Yingjie Cai +13
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI. Models such as SAM, DALL-E2, and GPT-4 showcase their adaptability by extrac…
RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation
Haiming Zhang, Xu Yan, Dongfeng Bai +5
3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images. However, image-based scene perception enc…
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
Haiming Zhang, Zhihao Yuan, Chaoda Zheng +6
Although existing speech-driven talking face generation methods achieve significant progress, they are far from real-world application due to the avatar-specific training demand an…
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Rui Sun, Andi Zhang, Haiming Zhang +5
Out-of-distribution (OOD) detection is a crucial task for ensuring the reliability and robustness of machine learning models. Recent works have shown that generative models often a…
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Chaoda Zheng, Xu Yan, Haiming Zhang +4
3D single object tracking in LiDAR point clouds (LiDAR SOT) plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance match…
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Chaoda Zheng, Xu Yan, Haiming Zhang +4
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving. Current approaches all follow the Siamese paradigm based on appearance matching…