Basic Information
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.
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.
Social
Publications
Authors:Deheng Zhang*, Jingyu Wang*, Shaofei Wang, Marko Mihajlovic, Sergey Prokudin, Hendrik P.A. Lensch, Siyu Tang (*equal contribution)
We present RISE-SDF, a method for reconstructing the geometry and material of glossy objects while achieving high-quality relighting.
Authors:Marko Mihajlovic, Sergey Prokudin, Siyu Tang, Robert Maier, Federica Bogo, Tony Tung, Edmond Boyer
SplatFields regularizes 3D gaussian splats for sparse 3D and 4D reconstruction.
Authors:Yan Zhang, Sergey Prokudin, Marko Mihajlovic, Qianli Ma, Siyu Tang
DOMA is an implicit motion field modeled by a spatiotemporal SIREN network. The learned motion field can predict how novel points move in the same field.
Authors:Xiyi Chen , Marko Mihajlovic , Shaofei Wang , Sergey Prokudin , Siyu Tang
We introduce a morphable diffusion model to enable consistent controllable novel view synthesis of humans from a single image. Given a single input image and a morphable mesh with a desired facial expression, our method directly generates 3D consistent and photo-realistic images from novel viewpoints, which we could use to reconstruct a coarse 3D model using off-the-shelf neural surface reconstruction methods such as NeuS2.
Authors:Zhiyin Qian, Shaofei Wang, Marko Mihajlovic, Andreas Geiger, Siyu Tang
Given a monocular video, 3DGS-Avatar learns clothed human avatars that model pose-dependent appearance and generalize to out-of-distribution poses, with short training time and interactive rendering frame rate.
ResFields: Residual Neural Fields for Spatiotemporal Signals
Conference: International Conference on Learning Representations (ICLR 2024) spotlight presentation
Authors:Marko Mihajlovic, Sergey Prokudin, Marc Pollefeys, Siyu Tang
ResField layers incorporates time-dependent weights into MLPs to effectively represent complex temporal signals.
Authors:Marko Mihajlovic, Aayush Bansal, Michael Zollhoefer, Siyu Tang, Shunsuke Saito
KeypointNeRF is a generalizable neural radiance field for virtual avatars.
Authors:Marko Mihajlovic , Shunsuke Saito , Aayush Bansal , Michael Zollhoefer and Siyu Tang
COAP is a novel neural implicit representation for articulated human bodies that provides an efficient mechanism for modeling self-contacts and interactions with 3D environments.
Authors:Shaofei Wang, Marko Mihajlovic, Qianli Ma, Andreas Geiger, Siyu Tang
MetaAvatar is meta-learned model that represents generalizable and controllable neural signed distance fields (SDFs) for clothed humans. It can be fast fine-tuned to represent unseen subjects given as few as 8 monocular depth images.
Authors:Marko Mihajlovic, Silvan Weder, Marc Pollefeys, Martin R. Oswald
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.
Authors:Marko Mihajlovic, Yan Zhang, Michael J. Black and Siyu Tang
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.