15 citations · 35 across the 8 of their papers we have counts for
11 papers
BlendFields: Few-Shot Example-Driven Facial Modeling
Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi +5
Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven…
VolTeMorph: Realtime, Controllable and Generalisable Animation of Volumetric Representations
Stephan J. Garbin, Marek Kowalski, Virginia Estellers +5
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 visu…
CoNeRF: Controllable Neural Radiance Fields
Kacper Kania, Kwang Moo Yi, Marek Kowalski +2
We extend neural 3D representations to allow for intuitive and interpretable user control beyond novel view rendering (i.e. camera control). We allow the user to annotate which par…
FastNeRF: High-Fidelity Neural Rendering at 200FPS
Stephan J. Garbin, Marek Kowalski, Matthew Johnson +2
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.…
TrajeVAE: Controllable Human Motion Generation from Trajectories
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. Current machine learning ap…
A high fidelity synthetic face framework for computer vision
Tadas Baltrusaitis, Erroll Wood, Virginia Estellers +6
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…
High Resolution Zero-Shot Domain Adaptation of Synthetically Rendered Face Images
Stephan J. Garbin, Marek Kowalski, Matthew Johnson +1
Generating photorealistic images of human faces at scale remains a prohibitively difficult task using computer graphics approaches. This is because these require the simulation of…
CONFIG: Controllable Neural Face Image Generation
Marek Kowalski, Stephan J. Garbin, Virginia Estellers +3
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 generativ…
HoloFace: Augmenting Human-to-Human Interactions on HoloLens
Marek Kowalski, Zbigniew Nasarzewski, Grzegorz Galinski +1
We present HoloFace, an open-source framework for face alignment, head pose estimation and facial attribute retrieval for Microsoft HoloLens. HoloFace implements two state-of-the-a…
Face Alignment Using K-Cluster Regression Forests With Weighted Splitting
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 initi…
Deep Alignment Network: A convolutional neural network for robust face alignment
Marek Kowalski, Jacek Naruniec, Tomasz Trzcinski
In this paper, we propose Deep Alignment Network (DAN), a robust face alignment method based on a deep neural network architecture. DAN consists of multiple stages, where each stag…