39 citations · 61 across the 19 of their papers we have counts for
8 papers · 1 filter
Seeing the Wind from a Falling Leaf
Zhiyuan Gao, Jiageng Mao, Hong-Xing Yu +5
A longstanding goal in computer vision is to model motions from videos, while the representations behind motions, i.e. the invisible physical interactions that cause objects to def…
ROVER: Benchmarking Reciprocal Cross-Modal Reasoning for Omnimodal Generation
Yongyuan Liang, Wei Chow, Feng Li +7
Unified multimodal models (UMMs) have emerged as a powerful paradigm for seamlessly unifying text and image understanding and generation. However, prevailing evaluations treat thes…
InstantSfM: Towards GPU-Native SfM for the Deep Learning Era
Jiankun Zhong, Zitong Zhan, Quankai Gao +6
Structure-from-Motion (SfM) is a fundamental technique for recovering camera poses and scene structure from multi-view imagery, serving as a critical upstream component for applica…
Learning an Implicit Physics Model for Image-based Fluid Simulation
Emily Yue-Ting Jia, Jiageng Mao, Zhiyuan Gao +2
Humans possess an exceptional ability to imagine 4D scenes, encompassing both motion and 3D geometry, from a single still image. This ability is rooted in our accumulated observati…
PhysBench: Benchmarking and Enhancing Vision-Language Models for Physical World Understanding
Wei Chow, Jiageng Mao, Boyi Li +3
Understanding the physical world is a fundamental challenge in embodied AI, critical for enabling agents to perform complex tasks and operate safely in real-world environments. Whi…
DreamDrive: Generative 4D Scene Modeling from Street View Images
Jiageng Mao, Boyi Li, Boris Ivanovic +7
Synthesizing photo-realistic visual observations from an ego vehicle's driving trajectory is a critical step towards scalable training of self-driving models. Reconstruction-based…
A Language Agent for Autonomous Driving
Jiageng Mao, Junjie Ye, Yuxi Qian +2
Human-level driving is an ultimate goal of autonomous driving. Conventional approaches formulate autonomous driving as a perception-prediction-planning framework, yet their systems…
GPT-Driver: Learning to Drive with GPT
Jiageng Mao, Yuxi Qian, Junjie Ye +2
We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge i…