Mohamed Hassan

As an AI Scientist at Electronic Arts, I specialize in developing advanced 3D generative models. My primary focus is on creating high-quality 3D assets, such as shapes, characters, and motion sequences, derived from various high-level control signals. These control signals can originate from images, videos, sketches, or text descriptions.

I hold a Ph.D. from Max Planck Institute for Intelligent Systems, where I worked on computer vision, computer graphics and machine learning. I was advised by Michael Black. In my PhD thesis, I explored the reconstruction, analysis, and generation of Human-Scene Interaction.

During my Ph.D., I was an intern at Nvidia Toronto AI lab where I worked with Sanja Filder and Jason Peng. I also had the pleasure of doing an internship at Adobe Research in the summer of 2020. At Adobe, I worked with Duygu Ceylan, Ruben Villegas, Jun Saito, Yi Zhou, and Jimei Yang.

In my free time, I enjoy reading about history, philosophy, and economics. I also like to cycle, run, swim, train BJJ, or go backpacking.

Email / CV / Google Scholar / Twitter / Github

News

December 2023: I gave an invited talk at the Univesity of Alberta..
September 2022: I joined Electronic Arts as an AI scientist working on character animation.
June 2021: I gave a talk about computer vision applications at the Sudanse Machine Learning Community.
June 2021: I joined Nvidia Toronto AI lab led by Sanja Filder for an internship.

Synthesizing Physical Character-Scene Interactions
Mohamed Hassan, Yunrong Guo, Tingwu Wang, Michael Black, Sanja Fidler, Xue Bin Peng
SIGGRAPH , 2023

Generating physical and realistic human-scene interactions without manual labeling. Generalize beyond the objects and scenarios depicted in the training dataset.

	Stochastic Scene-Aware Motion Prediction Mohamed Hassan, Duygu Ceylan, Ruben Villegas, Jun Saito, Jimei Yang, Yi Zhou, Michael Black ICCV, 2021 Generating realistic and diverse styles of human-scene interactions.
	Populating 3D Scenes by Learning Human-Scene Interaction Mohamed Hassan, Partha Ghosh, Joachim Tesch, Dimitrios Tzionas, Michael Black CVPR, 2021 Automatically place 3D scans of people in scenes.
	Generating 3D People in Scenes without People Yan Zhang, Mohamed Hassan, Michael Black, Siyu Tang CVPR, 2019 Generates 3D human bodies in 3D scenes.
	Resolving 3D Human Pose Ambiguities with 3D Scene Constraints Mohamed Hassan, Vasileios Choutas, Dimitrios Tzionas, Michael Black ICCV, 2019 Exploit static 3D scene structure to better estimate human pose from monocular images. Introduce the PROX dataset.
	Multiple proposals for continuous arabic sign language recognition Mohamed Hassan, Khaled Assaleh, Tamer Shanableh Sensing and Imaging, 2019 Comparison between two diferent recognition techniques for continuous Arabic Sign Language Recognition (ArSLR) for multiple users.
	User-dependent Sign Language Recognition Using Motion Detection Mohamed Hassan, Khaled Assaleh, Tamer Shanableh International Conference on Computational Science and Computational Intelligence, 2016 Continuous sensor-based Arabic Sign Language Recognition (ArSLR) system based on Hidden Markov Models(HMM) and a modified version of k-nearest neighbor(KNN).

Cool website template