Ehsan Pajouheshgar

I am currently a 3rd year PhD candidate at EPFL Lausanne pursuing my doctoral studies under supervision of Sabine Süsstrunk at Image and Visual Representation Laboratory (IVRL) . Before joining EPFL, I did my bachelor's degree in Computer Engineering at Sharif University of Technology in Iran.

During the summer of 2018, I had the opportunity to undertake an internship at IST Austria where I was mentored by Christoph Lampert. In the following year, from 2019 to 2020, I worked as a Data Scientist at Balad, a dynamic company dedicated to the development of advanced map and navigation software.

During my high school years, I developed a strong passion for Physics and dedicated considerable effort to preparing for the national physics Olympiad, where I was able to achieve the 7th rank among a vast pool of nearly 100 thousand participants and earn a gold medal.
Throughout my bachelor's degree, I found myself deeply intrigued by the world of probability and statistics. My interest led me to take part in the national statistics Olympiad, where I achieved the 1st place among hundreds of participants and awarded a gold medal.

Email / CV / Google Scholar / Github

Research

I'm interested in Computer Vision and Computer Graphics, and Machine Learning. I like to design simple (physics and bio)-inspired models to efficiently solve different tasks in Computer Vision and Computer Graphics. At the moment, my research is focused on

Neural cellular automata models and self-organizing systems
Texture Synthesis
Role of textures in visual perception

Publications
	DyNCA: Real-time Dynamic Texture Synthesis Using Neural Cellular Automata Ehsan Pajouheshgar, Yitao Xu, Tong Zhang, Sabine Süsstrunk CVPR, 2023 Demo / arXiv / code Dynamic Neural Cellular Automata (DyNCA), is a method that enables real-time and controllable dynamic texture synthesis. Checkout the demo!
	CLIPasso: Semantically-Aware Object Sketching Yael Vinker, Ehsan Pajouheshgar, Jessica Y. Bo, Roman Bachmann, Amit Haim Bermano, Daniel Cohen-Or, Amir Zamir, Ariel Shamir SIGGRAPH, 2022 (Best Paper Award) Project Page / Demo / arXiv / code Our work converts an image of an object to a sketch, allowing for varying levels of abstraction, while preserving its key visual features.
	Optimizing Latent Space Directions For GAN-based Local Image Editing Ehsan Pajouheshgar, Tong Zhang, Sabine Süsstrunk ICASSP, 2022 arXiv / code We introduce the Locally Effective Latent Space Direction (LELSD) framework, a novel approach to localized image editing in Generative Adversarial Networks (GANs), which utilizes a new objective function incorporating supervision from a pre-trained segmentation network.
	Back to square one: probabilistic trajectory forecasting without bells and whistles Ehsan Pajouheshgar, Christoph H. Lampert NeurIPS 2018, (Workshop on Modeling and Decision-Making in the Spatiotemporal Domain) arXiv We present an uncomplicated non-parametric baseline that attains the lowest error based on a widely adopted metric within the field. This serves to demonstrate the misleading nature of this particular metric.

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