Biography




Hello!

I am a Ph.D. researcher in Perceiving Systems at the MPI for Intelligent Systems, advised by Professor Michael J. Black and Dr. Dimitrios Tzionas.

I am interested in generating Virtual Humans that move and interact with 3D scenes and objects like Real Humans.

My broad research view includes: precise MoCap using multimodal sensors (IMUs, Cameras, Pressure Sensors, etc), 3D reconstruction from images, Human-Scene Interaction, and 3D representation.

Feel free to contact me about my research, collaborations, or anything else here.

Interests
  • Virtual Avatar Animation
  • Human-Object Interaction
  • Grasp Prediction
  • Motion Synthesis and Tracking
  • Object-Interaction Detection
  • 3D Geometry Representation
Education

  • Ph.D. in Computer Science - Machine Learning and Computer Vision, July 2018–present

    Max Planck Institute for Intelligent Systems, Tuebingen, DE.

  • M.Sc. in Mechanical Engineering - Applied Mechanics (Control and Robotic), 2014-2017

    Sharif University of Technology, Tehran, IR.

  • B.Sc. in Mechanical Engineering - Mechatronics, 2010-2014

    Isfahan University of Technology, Isfahan, IR

Publications

For a complete and up-to-date list of publications, check out my Google Scholar.

Quickly discover relevant content by filtering publications.
GRIP: Generating Interaction Poses Conditioned on Object and Body Motion
Abstract Hands are dexterous and highly versatile manipulators, that are central to how humans interact with objects and their environment. Consequently, modeling realistic hand object interactions, including the subtle motion of individual fingers, is critical for applications in computer graphics, computer vision, and mixed reality.
Omid Taheri, Yi Zhou, Dimitrios Tzionas, Yang Zhou, Duygu Ceylan, Soren Pirk, Michael J. Black
GRIP: Generating Interaction Poses Conditioned on Object and Body Motion
ARCTIC: Articulated Objects in Free-form Hand Interaction
Abstract We use our hands to interact with and to manipulate objects. Articulated objects are especially interesting since they often require the full dexterity of human hands to manipulate them. To understand, model, and synthesize such interactions, automatic and robust methods that reconstruct hands and articulated objects in 3D from a color image are needed.
Zicong Fan, Omid Taheri, Dimitrios Tzionas, Muhammed Kocabas, Manuel Kaufmann, Michael J. Black, Otmar Hilliges
PDF Cite Project Video CVPR2023 Conference
ARCTIC: Articulated Objects in Free-form Hand Interaction
IPMAN: 3D Human Pose Estimation via Intuitive Physics
Abstract The estimation of 3D human body shape and pose from images has advanced rapidly. While the results are often well aligned with image features in the camera view, the 3D pose is often physically implausible; bodies lean, float, or penetrate the floor.
Shashank Tripathi, Lea Müller, Chun-Hao P. Huang, Omid Taheri, Michael Black, Dimitrios Tzionas
PDF Cite Project Video CVPR2023 Conference
IPMAN: 3D Human Pose Estimation via Intuitive Physics
InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction
Abstract Humans constantly interact with daily objects to accomplish tasks. To understand such interactions, computers need to reconstruct these from cameras observing whole-body interaction with scenes. This is challenging due to occlusion between the body and objects, motion blur, depth/scale ambiguities, and the low image resolution of hands and graspable object parts.
Yinghao Huang, Omid Taheri, Michael J. Black, Dimitrios Tzionas
PDF Cite Code Dataset Poster Video GCPR2022 Conference
InterCap: Joint Markerless 3D Tracking of Humans and Objects in Interaction
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping
Abstract Generating digital humans that move realistically has many applications and is widely studied, but existing methods focus on the major limbs of the body, ignoring the hands and head. Hands have been separately studied but the focus has been on generating realistic static grasps of objects.
Omid Taheri, Vasileios Choutas, Michael J. Black, Dimitrios Tzionas
PDF Cite Code Dataset Poster Video CVPR2022 Conference
GOAL: Generating 4D Whole-Body Motion for Hand-Object Grasping
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Abstract Training computers to understand, model, and synthesize human grasping requires a rich dataset containing complex 3D object shapes, detailed contact information, hand pose and shape, and the 3D body motion over time.
Omid Taheri, Nima Ghorbani, Michael J. Black, Dimitrios Tzionas
PDF Cite Code Dataset Video Source Document ECCV2020 Conference
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter
Abstract Human motion capture is frequently used to study rehabilitation and clinical problems, as well as to provide realistic animation for the entertainment industry. IMU-based systems, as well as Markerbased motion tracking systems, are most popular methods to track movement due to their low cost of implementation and lightweight.
Omid Taheri, Hassan Salarieh, Aria Alasty
PDF Cite
Human Leg Motion Tracking by Fusing IMUs and RGB Camera Data Using Extended Kalman Filter

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