activity
20172020
most citedReal-time Facial Surface Geometry from Monocular Video on Mobile GPUs

58 citations · 177 across the 8 of their papers we have counts for

collaborators

9 papers

cs.CV2020

Objectron: A Large Scale Dataset of Object-Centric Videos in the Wild with Pose Annotations

Adel Ahmadyan, Liangkai Zhang, Jianing Wei +2

3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advanc…

cs.CV2020★ 9 cited

Instant 3D Object Tracking with Applications in Augmented Reality

Adel Ahmadyan, Tingbo Hou, Jianing Wei +3

Tracking object poses in 3D is a crucial building block for Augmented Reality applications. We propose an instant motion tracking system that tracks an object's pose in space (repr…

cs.CV2020★ 18 cited

Real-time Pupil Tracking from Monocular Video for Digital Puppetry

Artsiom Ablavatski, Andrey Vakunov, Ivan Grishchenko +2

We present a simple, real-time approach for pupil tracking from live video on mobile devices. Our method extends a state-of-the-art face mesh detector with two new components: a ti…

cs.CV2020★ 52 cited

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

Ivan Grishchenko, Artsiom Ablavatski, Yury Kartynnik +2

We present Attention Mesh, a lightweight architecture for 3D face mesh prediction that uses attention to semantically meaningful regions. Our neural network is designed for real-ti…

cs.PF2020

The Two-Pass Softmax Algorithm

Marat Dukhan, Artsiom Ablavatski

The softmax (also called softargmax) function is widely used in machine learning models to normalize real-valued scores into a probability distribution. To avoid floating-point ove…

cs.CV2019★ 8 cited

Real-time Hair Segmentation and Recoloring on Mobile GPUs

Andrei Tkachenka, Gregory Karpiak, Andrey Vakunov +4

We present a novel approach for neural network-based hair segmentation from a single camera input specifically designed for real-time, mobile application. Our relatively small neur…