Marko Mihajlovic

I am a PhD student in the VLG group at ETH Zurich since September 2020.
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.

I was a research intern at Meta Reality Labs, hosted by Michael Zollhoefer. I obtained my Master's degree at ETH where I conducted research at CVG.

Email / Google Scholar / Twitter / LinkedIn / Github

News

07/2022: Our KeypointNeRF is accepted at ECCV 2022!
03/2022: Our COAP is accepted at CVPR 2022!
09/2021: Joined Meta Reality Labs as a research intern (hosted by Michael Zollhoefer).
03/2021: Two papers accepted at CVPR 2021 (LEAP, DeepSurfels)!
09/2020: Joined VLG as a PhD student (supervisor Siyu Tang).
09/2020: Completed a two-year Master program at the Instituate for Visual Computing (supervisor Marc Pollefeys).
09/2018: I started Direct Doctorate in Computer Science at ETH Zurich.

Publications

KeypointNeRF: Generalizing Image-based Volumetric Avatars using Relative Spatial Encoding of Keypoints
Marko Mihajlovic, Aayush Bansal, Michael Zollhoefer, Siyu Tang, Shunsuke Saito
ECCV, 2022
project page / arXiv / code / video

KeypointNeRF is a generalizable neural radiance field for virtual avatars. Given as input 2-3 images, KeypointNeRF generates volumetric radiance representation that can be rendered from novel views.

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.