I am a researcher specializing in digital humans, with a strong emphasis on deep learning, computer graphics, and computer vision.
Previously, I was at |
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Niessner Lab |
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NCS Group |
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Reality Labs |
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Syntec |
We present SynShot, a novel method for the few-shot inversion of a drivable head avatar based on a synthetic prior. We tackle two major challenges. First, state-of-the-art monocular avatar models struggle to generalize to new views and expressions, lacking a strong prior and often overfitting to a specific viewpoint distribution....
Current personalized neural head avatars face a trade-off: lightweight models lack detail and realism, while high-quality, animatable avatars require significant computational resources, making them unsuitable for commodity devices. To address this gap, we introduce Gaussian Eigen Models (GEM), which provide high-quality, lightweight, and easily controllable head avatars. GEM utilizes 3D...
We present Drivable 3D Gaussian Avatars (D3GA), a multi-layered 3D controllable model for human bodies that utilizes 3D Gaussian primitives embedded into tetrahedral cages. The advantage of using cages compared to commonly employed linear blend skinning (LBS) is that primitives like 3D Gaussians are naturally re-oriented and their kernels are...
Digital humans and, especially, 3D facial avatars have raised a lot of attention in the past years, as they are the backbone of several applications like immersive telepresence in AR or VR. Despite the progress, facial avatars reconstructed from commodity hardware are incomplete and miss out on parts of the...
For immersive telepresence in AR or VR, we aim for digital humans (avatars) that mimic the motions and facial expressions of the actual subjects participating in a meeting. Besides the motion, these avatars should reflect the human's shape and appearance. Instead of prerecorded, old avatars, we aim to instantaneously reconstruct...
Face reconstruction and tracking is a building block of numerous applications in AR/VR, human-machine interaction, as well as medical applications. Most of these applications rely on a metrically correct prediction of the shape, especially, when the reconstructed subject is put into a metrical context (i.e., when there is a reference...
This project is an engine where you can create/test new shaders in real-time physically based ray tracing system. The main idea is that users can test their own integrators in predefined environment with assets like: meshes, materials, lights and textures. The Vulkan pipeline is abstracted in a shaders form and...
CPU implementation of a very efficient rasterizer which utilizes AVX2 instructions and lock-free multi-threading programming. Using tiled rendering the program consumes 8 pixels at the same time, which together with std::execution threading library makes it very fast and robust. The viewer of the project is a very simple OpenGL applicatoin...
This work elaborates a parallel algorithm based on quadric error metric and adaptive thresholding to simplify a triangle mesh. The approach emphasizes planar surfaces as a target to simplify. The main goal was to create a framework able to produce high-quality progressive meshes based on reconstructed ones from the environment....
This work is a master thesis which proposes a novel way of synthesizing audio-driven portrait videos. We show that photo-realistic images can be rendered based on a small, fully connected neural network with the positional encoding of 3D face surface and additional audio-features extracted from an arbitrary English speech. The...
Photometric based markerless facial tracking using analysis-by-synthesis approach for 3DMMs face parameters optimization. All the computational parts of our pipeline were ran on GPU using CUDA and OpenGL. Our energy function consists of a sparse landmark term, a dense photometric term and a regularizer term. This energy function is minimized...