Marko Mihajlovic

I am a PhD student at ETH Zurich supervised by Prof. Dr. Siyu Tang since September 2020. Previously, I obtained my Master's degree at ETH as a part of the Direct Doctorate in Computer Science and completed undergraduate studies at the Faculty of Electronic Engineering, University of Nis.

My research lies at the intersection of computer vision, machine learning, and computer graphics. I am particularly interested in realistic reconstruction of the 3D world around us and understanding how we, as humans, interact with the environment.

Email / Google Scholar / Twitter / LinkedIn / Github

News

01.03.2021: Two papers accepted at CVPR 2021 (1, 2).
12.09.2020: I joined VLG as a PhD student (supervisor Siyu Tang).
10.09.2020: Completed a two-year Master program at the Instituate for Visual Computing (supervisor Marc Pollefeys).
17.09.2018: I started Direct Doctorate in Computer Science at ETH Zurich.

Publications

COAP: Compositional Articulated Occupancy of People
Marko Mihajlovic, Shunsuke Saito, Aayush Bansal, Michael Zollhoefer, Siyu Tang
CVPR, 2022
project page / arXiv / code / video

COAP is a novel neural implicit representation for articulated human bodies that provides an efficient mechanism for modeling self-contact and interactions with the environment.

MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images
Shaofei Wang, Marko Mihajlovic, Qianli Ma, Andreas Geiger, Siyu Tang
NeurIPS, 2021
project page / arXiv / code / video

Generalizable and controllable neural signed distance fields (SDFs) that represent clothed humans from monocular depth observations.

LEAP: Learning Articulated Occupancy of People
Marko Mihajlovic, Yan Zhang, Michael J. Black, Siyu Tang
CVPR, 2021
project page / arXiv / code / video

LEAP is a neural network architecture for representing volumetric animatable human bodies. It follows traditional human body modeling techniques and leverages a statistical human prior to generalize to unseen humans.

DeepSurfels: Learning Online Appearance Fusion
Marko Mihajlovic, Silvan Weder, Marc Pollefeys, Martin R. Oswald
CVPR, 2021
project page / arXiv / code / video

DeepSurfels is a novel 3D representation for geometry and appearance information that combines planar surface primitives with voxel grid representation for improved scalability and rendering quality.