Liu He

I am final year PhD candidate at Purdue University. My advisor is Daniel Aliaga. I am interested in integrating advanced techniques to broad interdisciplinary CV/CG problems. My research focuses on advancing 3D-aware Multimodal LLMs (LLaVA, etc.), Video MLLMs, generative models for image generation and editing, and broad topics on generation, reconstruction, and representation learning of 2D/3D layouts and scenes. I am actively looking for full-time jobs, and always open to any collaborations on interesting topics.

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!)
	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 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
	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