no code implementations • 13 Mar 2024 • Enric Corona, Andrei Zanfir, Eduard Gabriel Bazavan, Nikos Kolotouros, Thiemo Alldieck, Cristian Sminchisescu
We propose VLOGGER, a method for audio-driven human video generation from a single input image of a person, which builds on the success of recent generative diffusion models.
no code implementations • 4 Nov 2023 • Eduard Gabriel Bazavan, Andrei Zanfir, Thiemo Alldieck, Teodor Alexandru Szente, Mihai Zanfir, Cristian Sminchisescu
We present \emph{SPHEAR}, an accurate, differentiable parametric statistical 3D human head model, enabled by a novel 3D registration method based on spherical embeddings.
no code implementations • 11 Sep 2023 • Ivan Grishchenko, Geng Yan, Eduard Gabriel Bazavan, Andrei Zanfir, Nikolai Chinaev, Karthik Raveendran, Matthias Grundmann, Cristian Sminchisescu
We present Blendshapes GHUM, an on-device ML pipeline that predicts 52 facial blendshape coefficients at 30+ FPS on modern mobile phones, from a single monocular RGB image and enables facial motion capture applications like virtual avatars.
no code implementations • CVPR 2023 • Enric Corona, Mihai Zanfir, Thiemo Alldieck, Eduard Gabriel Bazavan, Andrei Zanfir, Cristian Sminchisescu
We introduce Structured 3D Features, a model based on a novel implicit 3D representation that pools pixel-aligned image features onto dense 3D points sampled from a parametric, statistical human mesh surface.
no code implementations • 23 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.
no code implementations • 23 Dec 2021 • Eduard Gabriel Bazavan, Andrei Zanfir, Mihai Zanfir, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu
We combine a hundred diverse individuals of varying ages, gender, proportions, and ethnicity, with hundreds of motions and scenes, as well as parametric variations in body shape (for a total of 1, 600 different humans), in order to generate an initial dataset of over 1 million frames.
Ranked #1 on 3D Human Pose Estimation on HSPACE
no code implementations • 29 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.
no code implementations • ICCV 2021 • Mihai Zanfir, Andrei Zanfir, Eduard Gabriel Bazavan, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu
We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images.
Ranked #40 on 3D Human Pose Estimation on 3DPW (MPJPE metric)
no code implementations • CVPR 2021 • Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Zanfir, William T. Freeman, Rahul Sukthankar, Cristian Sminchisescu
We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image.
Ranked #61 on 3D Human Pose Estimation on 3DPW (MPJPE metric)
no code implementations • ECCV 2020 • Andrei Zanfir, Eduard Gabriel Bazavan, Hongyi Xu, Bill Freeman, Rahul Sukthankar, Cristian Sminchisescu
Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervised learning in complex visual scenes.
Ranked #53 on 3D Human Pose Estimation on 3DPW (PA-MPJPE metric)