Search Results for author: Marek Kowalski

Found 12 papers, 6 papers with code

LumiGauss: High-Fidelity Outdoor Relighting with 2D Gaussian Splatting

1 code implementation6 Aug 2024 Joanna Kaleta, Kacper Kania, Tomasz Trzcinski, Marek Kowalski

Our approach yields high-quality scene reconstructions and enables realistic lighting synthesis under novel environment maps.

3D Reconstruction

BlendFields: Few-Shot Example-Driven Facial Modeling

no code implementations CVPR 2023 Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi, Virginia Estellers, Kwang Moo Yi, Julien Valentin, Tomasz Trzciński, Marek Kowalski

Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance.

VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations

no code implementations1 Aug 2022 Stephan J. Garbin, Marek Kowalski, Virginia Estellers, Stanislaw Szymanowicz, Shideh Rezaeifar, Jingjing Shen, Matthew Johnson, Julien Valentin

The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time.

Novel View Synthesis

CoNeRF: Controllable Neural Radiance Fields

1 code implementation CVPR 2022 Kacper Kania, Kwang Moo Yi, Marek Kowalski, Tomasz Trzciński, Andrea Tagliasacchi

We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i. e. camera control).

3D Face Modelling 3D Reconstruction +2

TrajeVAE: Controllable Human Motion Generation from Trajectories

no code implementations1 Apr 2021 Kacper Kania, Marek Kowalski, Tomasz Trzciński

The creation of plausible and controllable 3D human motion animations is a long-standing problem that requires a manual intervention of skilled artists.

Pose Prediction

FastNeRF: High-Fidelity Neural Rendering at 200FPS

1 code implementation ICCV 2021 Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton, Julien Valentin

Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints.

Mixed Reality Neural Rendering +1

A high fidelity synthetic face framework for computer vision

no code implementations16 Jul 2020 Tadas Baltrusaitis, Erroll Wood, Virginia Estellers, Charlie Hewitt, Sebastian Dziadzio, Marek Kowalski, Matthew Johnson, Thomas J. Cashman, Jamie Shotton

Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others.

Diversity Face Model +3

High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images

no code implementations ECCV 2020 Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton

In contrast to computer graphics approaches, generative models learned from more readily available 2D image data have been shown to produce samples of human faces that are hard to distinguish from real data.

Domain Adaptation Vocal Bursts Intensity Prediction

CONFIG: Controllable Neural Face Image Generation

2 code implementations ECCV 2020 Marek Kowalski, Stephan J. Garbin, Virginia Estellers, Tadas Baltrušaitis, Matthew Johnson, Jamie Shotton

Our ability to sample realistic natural images, particularly faces, has advanced by leaps and bounds in recent years, yet our ability to exert fine-tuned control over the generative process has lagged behind.

Attribute Face Model +2

HoloFace: Augmenting Human-to-Human Interactions on HoloLens

1 code implementation1 Feb 2018 Marek Kowalski, Zbigniew Nasarzewski, Grzegorz Galinski, Piotr Garbat

Head pose estimation is accomplished by fitting a deformable 3D model to the landmarks localized using face alignment.

Attribute Emotion Recognition +4

Face Alignment Using K-Cluster Regression Forests With Weighted Splitting

no code implementations6 Jun 2017 Marek Kowalski, Jacek Naruniec

In this work we present a face alignment pipeline based on two novel methods: weighted splitting for K-cluster Regression Forests and 3D Affine Pose Regression for face shape initialization.

Face Alignment regression

Deep Alignment Network: A convolutional neural network for robust face alignment

3 code implementations6 Jun 2017 Marek Kowalski, Jacek Naruniec, Tomasz Trzcinski

Our method uses entire face images at all stages, contrary to the recently proposed face alignment methods that rely on local patches.

Ranked #4 on Face Alignment on 300W Split 2 (NME (inter-ocular) metric)

Face Alignment Keypoint Detection +1

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