Search Results for author: Matthias Grundmann

Found 17 papers, 7 papers with code

BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation

no code implementations23 Jun 2022 Ivan Grishchenko, Valentin Bazarevsky, Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir, Richard Yee, Karthik Raveendran, Matsvei Zhdanovich, Matthias Grundmann, Cristian Sminchisescu

We present BlazePose GHUM Holistic, a lightweight neural network pipeline for 3D human body landmarks and pose estimation, specifically tailored to real-time on-device inference.

3D Human Pose Estimation

On-device Real-time Hand Gesture Recognition

no code implementations29 Oct 2021 George Sung, Kanstantsin Sokal, Esha Uboweja, Valentin Bazarevsky, Jonathan Baccash, Eduard Gabriel Bazavan, Chuo-Ling Chang, Matthias Grundmann

We present an on-device real-time hand gesture recognition (HGR) system, which detects a set of predefined static gestures from a single RGB camera.

Hand Gesture Recognition Hand-Gesture Recognition

On the Estimation of the Number of Unreachable Peers in the Bitcoin P2P Network by Observation of Peer Announcements

no code implementations25 Feb 2021 Matthias Grundmann, Hedwig Amberg, Hannes Hartenstein

Thus, the number of unreachable peers can only be estimated based on some indicators.

Cryptography and Security Networking and Internet Architecture

Attention Mesh: High-fidelity Face Mesh Prediction in Real-time

1 code implementation19 Jun 2020 Ivan Grishchenko, Artsiom Ablavatski, Yury Kartynnik, Karthik Raveendran, Matthias Grundmann

We present Attention Mesh, a lightweight architecture for 3D face mesh prediction that uses attention to semantically meaningful regions.

MediaPipe Hands: On-device Real-time Hand Tracking

4 code implementations18 Jun 2020 Fan Zhang, Valentin Bazarevsky, Andrey Vakunov, Andrei Tkachenka, George Sung, Chuo-Ling Chang, Matthias Grundmann

We present a real-time on-device hand tracking pipeline that predicts hand skeleton from single RGB camera for AR/VR applications.

BlazePose: On-device Real-time Body Pose tracking

7 code implementations17 Jun 2020 Valentin Bazarevsky, Ivan Grishchenko, Karthik Raveendran, Tyler Zhu, Fan Zhang, Matthias Grundmann

We present BlazePose, a lightweight convolutional neural network architecture for human pose estimation that is tailored for real-time inference on mobile devices.

3D Human Pose Estimation 3D Pose Estimation +3

Real-time Facial Surface Geometry from Monocular Video on Mobile GPUs

3 code implementations15 Jul 2019 Yury Kartynnik, Artsiom Ablavatski, Ivan Grishchenko, Matthias Grundmann

The relatively dense mesh model of 468 vertices is well-suited for face-based AR effects.

BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs

9 code implementations11 Jul 2019 Valentin Bazarevsky, Yury Kartynnik, Andrey Vakunov, Karthik Raveendran, Matthias Grundmann

We present BlazeFace, a lightweight and well-performing face detector tailored for mobile GPU inference.

Face Detection

On-Device Neural Net Inference with Mobile GPUs

no code implementations3 Jul 2019 Juhyun Lee, Nikolay Chirkov, Ekaterina Ignasheva, Yury Pisarchyk, Mogan Shieh, Fabio Riccardi, Raman Sarokin, Andrei Kulik, Matthias Grundmann

On-device inference of machine learning models for mobile phones is desirable due to its lower latency and increased privacy.

MediaPipe: A Framework for Building Perception Pipelines

2 code implementations14 Jun 2019 Camillo Lugaresi, Jiuqiang Tang, Hadon Nash, Chris McClanahan, Esha Uboweja, Michael Hays, Fan Zhang, Chuo-Ling Chang, Ming Guang Yong, Juhyun Lee, Wan-Teh Chang, Wei Hua, Manfred Georg, Matthias Grundmann

A developer can use MediaPipe to build prototypes by combining existing perception components, to advance them to polished cross-platform applications and measure system performance and resource consumption on target platforms.

Distributed, Parallel, and Cluster Computing

Finding Temporally Consistent Occlusion Boundaries in Videos using Geometric Context

no code implementations25 Oct 2015 S. Hussain Raza, Ahmad Humayun, Matthias Grundmann, David Anderson, Irfan Essa

Our proposed framework provides an efficient approach for finding temporally consistent occlusion boundaries in video by utilizing causality, redundancy in videos, and semantic layout of the scene.

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