Liu He

I am an applied scientist at Amazon Ring AI, working on enhancing MLLMs in-the-wild. I received my CS PhD at Purdue University, under Professor Daniel Aliaga. My research integrates cutting-edge techniques to solve interdisciplinary problems across CV/CG.

My passion is to well embed the physical world. Now my research focusing on synthetic data generation, advancing MLLM reasoning on visual tasks, and physcial-aware VideoGen as World Model.

I am open to collaborations and internships. Please feel free to contact me if we have common interest.

Email / CV / GitHub / Google Scholar / LinkedIn

Research

	Advancing MLLMs by Large-Scale 3D Visual Instruction Dataset Generation Liu He, Xiao Zeng, Yizhi Song, Albert Y. C. Chen, Lu Xia, Shashwat Verma, Sankalp Dayal, Min Sun, Daniel Aliaga Under Reviewing Project / Paper (Coming Soon!)
	Kubrick: Multimodal Agent Collaborations for Synthetic Video Generation Liu He, Yizhi Song, Hejun Huang, Daniel Aliaga, Xin Zhou Under Reviewing Project / Paper
	Refine-by-Align: Reference-Guided Artifacts Refinement through Semantic Alignment Yizhi Song, Liu He, Zhifei Zhang, Soo Ye Kim, He Zhang, Wei Xiong, Zhe Lin, Brian Price, Scott Cohen, Jianming Zhang, Daniel Aliaga ICLR 2025 Project / Paper
	COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation Liu He, Daniel Aliaga ECCV 2024 Oral Project / Paper / Code / Dataset
	GlobalMapper: Arbitrary-Shaped Urban Layout Generation Liu He, Daniel Aliaga ICCV 2023 Project / Paper / Supp / Code
	Diffusion-Based Document Layout Generation Liu He, Yijuan Lu, John Corring, Dinei Florencio, Cha Zhang ICDAR 2023 Oral Project / Paper / Code
	Generative Building Feature Estimation from Satellite Images Liu He, Jie Shan, Daniel Aliaga IEEE Transactions on Geoscience and Remote Sensing (2023) Project / Paper
	Deep Learning-Based Urban Morphology for City-Scale Environmental Modeling Pratiman Patel, Rajesh Kalyanam, Liu He, Daniel Aliaga, Dev Niyogi PNAS Nexus (2023) Project / Paper