Difan Liu

I am a research scientist at Adobe Research, where I work on computer graphics, computer vision and machine learning.

I received my PhD from UMass Amherst advised by Evangelos Kalogerakis. Prior to UMass Amherst, I got my bachelor's degree with honors in Electrical Engineering from University of Science and Technology of China.

Internships: I'm always happy to host research interns at Adobe Research. If you are interested in working with me, please send me an email describing your past experience and current research interests.

Email / Google Scholar / Github

Research

My research interests include generative models, vector graphics and video generation. I am particularly interested in the synthesis and editing of images, video and vector graphics based on machine learning.

	Customize-A-Video: One-Shot Motion Customization of Text-to-Video Diffusion Models Yixuan Ren, Yang Zhou, Jimei Yang, Jing Shi, Difan Liu, Feng Liu, Mingi Kwon, Abhinav Shrivastava ECCV, 2024 Project
	HARIVO: Harnessing Text-to-Image Models for Video Generation Mingi Kwon, Seoung Wug Oh, Yang Zhou, Difan Liu, Joon-Young Lee, Haoran Cai, Baqiao Liu, Feng Liu, Youngjung Uh ECCV, 2024 Project
	VecFusion: Vector Font Generation with Diffusion Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michaël Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis (equal contribution) CVPR, 2024 (Highlight)* PDF
	NIVeL: Neural Implicit Vector Layers for Text-to-Vector Generation Vikas Thamizharasan, Difan Liu, Matthew Fisher, Nanxuan Zhao, Evangelos Kalogerakis, Michal Lukáč CVPR, 2024 PDF
	Visual Layout Composer: Image-Vector Dual Diffusion Model for Design Layout Generation Mohammad Amin Shabani, Zhaowen Wang, Difan Liu, Nanxuan Zhao, Jimei Yang, Yasutaka Furukawa CVPR, 2024 Project
	Attention-Driven Training-Free Efficiency Enhancement of Diffusion Models Hongjie Wang, Difan Liu, Yan Kang, Yijun Li, Zhe Lin, Niraj Jha, Yuchen Liu CVPR, 2024 PDF
	SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model Zhengang Li, Yan Kang, Yuchen Liu, Difan Liu, Tobias Hinz, Feng Liu, Yanzhi Wang CVPR, 2024 PDF
	LRM: Large Reconstruction Model for Single Image to 3D Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, Hao Tan ICLR, 2024 (Oral Presentation) PDF / Project
	ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions Difan Liu, Sandesh Shetty, Tobias Hinz, Matthew Fisher, Richard Zhang, Taesung Park, Evangelos Kalogerakis SIGGRAPH - Journal Track, 2022 PDF(low-res) / PDF(high-res) / Project
	Neural Strokes: Stylized Line Drawing of 3D Shapes Difan Liu, Matthew Fisher, Aaron Hertzmann, Evangelos Kalogerakis ICCV, 2021 PDF / Project
	Neural Contours: Learning to Draw Lines from 3D Shapes Difan Liu, Mohamed Nabail, Aaron Hertzmann, Evangelos Kalogerakis CVPR, 2020 PDF / Project
	ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds Gopal Sharma, Difan Liu, Subhransu Maji, Evangelos Kalogerakis, Siddhartha Chaudhuri, Radomír Měch ECCV, 2020 PDF / Project
	Neural Shape Parsers for Constructive Solid Geometry Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji TPAMI, 2020 PDF / Code
	Deep Part Induction from Articulated Object Pairs Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas SIGGRAPH Asia, 2018 PDF / Code
	CSGNet: Neural Shape Parser for Constructive Solid Geometry Gopal Sharma, Rishabh Goyal, Difan Liu, Evangelos Kalogerakis, Subhransu Maji CVPR, 2018 PDF / Code

Product Impacts, Tech Transfers, and Demos

Firefly Text to Image in Express and Firefly, 2023

Project Glyph Ease at Adobe MAX Sneaks, 2023

Project Res Up at Adobe MAX Sneaks, 2023

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