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.
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02/2024: Three papers accepted at CVPR 2024!
01/2024: ResFields is accepted at ICLR 2024 as a spotlight paper!
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).
Morphable diffusion enables consistent controllable novel view synthesis of humans from a single image.
Given a monocular video, 3DGS-Avatar learns a clothed human avatars with short training time and interactive rendering frame rate.
ResField layers incorporates time-dependent weights into MLPs to effectively represent complex temporal signals.
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 is a novel neural implicit representation for articulated human bodies that provides an efficient mechanism for modeling self-contact and interactions with the environment.
Generalizable and controllable neural signed distance fields (SDFs) that represent clothed humans from monocular depth observations.
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 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.