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Christoph Lassner

Research Scientist

Meta Reality Labs Research

Biography

I am a Machine Learning and Computer Vision researcher making machines understand and generate representations of humans in 2D and 3D. I joined Meta Reality Labs Research in March 2019 to make my vision of better and easier interaction between humans and machines become a reality.

Before joining Meta, I worked at a startup which was acquired by Amazon. I did my PhD at the Bernstein Center for Computational Neuroscience Tübingen and the Max Planck Institute for Intelligent Systems.

Interests

  • Machine Learning
  • 2D and 3D Human Body Modelling
  • Differentiable and Neural Rendering

Featured Publications

Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhoefer, Jessica Hodgins, Christoph Lassner
June 2022

HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture

We create a metrically accurate hair representation using neural rendering primitives and optical flow.

PDF Project
Hsiao-yu Chen, Edith Tretschk, Tuur Stuyck, Petr Kadlecek, Ladislav Kavan, Etienne Vouga, Christoph Lassner
June 2022

Virtual Elastic Objects

We create virtual objects that visually and physically resemble their real counterparts.

PDF
Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edith Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricarco Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Nießner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhöfer, Vladislav Golyanik
April 2022

Advances in Neural Rendering

We present a state-of-the-art report on the latest advances in neural rendering with a focus on methods that combine classical rendering principles with learned 3D scene representations.

PDF
Christoph Lassner, Michael Zollhöfer
June 2021

Pulsar: Efficient Sphere-based Neural Rendering

A very efficient differentiable renderer, tightly integrated with PyTorch and PyTorch3D.

PDF Code

Recent Publications

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HVH: Learning a Hybrid Neural Volumetric Representation for Dynamic Hair Performance Capture

We create a metrically accurate hair representation using neural rendering primitives and optical flow.
Ziyan Wang, Giljoo Nam, Tuur Stuyck, Stephen Lombardi, Michael Zollhoefer, Jessica Hodgins, Christoph Lassner
PDF Project

Virtual Elastic Objects

We create virtual objects that visually and physically resemble their real counterparts.
Hsiao-yu Chen, Edith Tretschk, Tuur Stuyck, Petr Kadlecek, Ladislav Kavan, Etienne Vouga, Christoph Lassner
PDF

Neural 3D Video Synthesis

We propose a novel approach for 3D video synthesis that is able to represent multi-view video recordings of a dynamic real-world scene in a compact, yet expressive representation that enables high-quality view synthesis and motion interpolation.
Tianye Li, Mira Slavcheva, Michael Zollhöfer, Simon Green, Christoph Lassner, Changil Kim, Tanner Schmidt, Simon Lovegrove, Michael Gösele, Richard Newcombe, Zhaoyang Lv
PDF Project Video

Self-supervised Neural Articulated Shape and Appearance Models

We propose a novel approach for learning a representation of the geometry, appearance, and motion of a class of articulated objects given only a set of color images as input.
Fangyin Wei, Rohan Chabra, Lingni Ma, Christoph Lassner, Michael Zollhöfer, Syzmon Rusinkiewicz, Chris Sweeney, Richard Newcombe, Mira Slavcheva
Project

Advances in Neural Rendering

We present a state-of-the-art report on the latest advances in neural rendering with a focus on methods that combine classical rendering principles with learned 3D scene representations.
Ayush Tewari, Justus Thies, Ben Mildenhall, Pratul Srinivasan, Edith Tretschk, Yifan Wang, Christoph Lassner, Vincent Sitzmann, Ricarco Martin-Brualla, Stephen Lombardi, Tomas Simon, Christian Theobalt, Matthias Nießner, Jonathan T. Barron, Gordon Wetzstein, Michael Zollhöfer, Vladislav Golyanik
PDF
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