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Parham Saremi

M.Sc. Student @ McGill/Mila

I'm a Masters student in the Probabilistic Vision Group at McGill University under the supervision of Professor Tal Arbel.

You can find my complete CV here.

Research Interests

  • Neuroimage Analysis
  • Deep Learning
  • Generative Modeling
  • Representational learning
  • Computer vision

Education

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    M.Sc. Electrical Engineering

    McGill University, Montreal, Canada

  • sharif logo

    B.Sc Computer Engineering

    Sharif University of Technology, Tehran, Iran

Experience

  • ETH logo

    Research Intern

    ETH, Zurich

  • EPFL logo

    Research Intern

    EPFL, Lausanne

  • UCI logo

    Undergraduate Researcher

    UCI, California


Toward reliable human pose forecasting with uncertainty

In our IEEE RA-L paper, we address the challenge of predicting future 3D human poses from past observations. We introduce an open-source library for human pose forecasting, offering multiple models, standardized evaluation metrics, and support for various datasets. Our methods improve forecasting accuracy by modeling both aleatoric and epistemic uncertainty. Experiments on Human3.6M, AMSS, and 3DPW datasets show up to 25% improvement in short-term forecasting without compromising long-term performance. Our code is available online to facilitate further research.

EPFL VITA pose prediction project

Reconstruction of 3D Interaction Models from Images using Shape Prior

In our ICCV workshop paper, we explore the reconstruction of 3D human-object interactions from images, including both human and object shape and pose estimation. We introduce a novel autoregressive transformer-based variational autoencoder that learns a robust shape prior from extensive 3D datasets. By leveraging the reconstructed 3D human body, our approach enhances object shape and pose estimation. Experimental results on the BEHAVE dataset demonstrate the effectiveness of our method, achieving a 40.7 cm Chamfer distance and showcasing the benefits of learning a shape prior.

ETH 3d reconstruction paper

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