no code implementations • CVPR 2017 • Alin-Ionut Popa, Mihai Zanfir, Cristian Sminchisescu
We propose a deep multitask architecture for \emph{fully automatic 2d and 3d human sensing} (DMHS), including \emph{recognition and reconstruction}, in \emph{monocular images}.
Ranked #21 on 3D Human Pose Estimation on HumanEva-I
no code implementations • 17 Oct 2016 • Mihai Zanfir, Elisabeta Marinoiu, Cristian Sminchisescu
Automatic video captioning is challenging due to the complex interactions in dynamic real scenes.
no code implementations • NeurIPS 2018 • Andrei Zanfir, Elisabeta Marinoiu, Mihai Zanfir, Alin-Ionut Popa, Cristian Sminchisescu
The final stage of 3d pose and shape prediction is based on a learned attention process where information from different human body parts is optimally integrated.
no code implementations • CVPR 2018 • Elisabeta Marinoiu, Mihai Zanfir, Vlad Olaru, Cristian Sminchisescu
We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism.
no code implementations • CVPR 2018 • Mihai Zanfir, Alin-Ionut Popa, Andrei Zanfir, Cristian Sminchisescu
We propose an automatic person-to-person appearance transfer model based on explicit parametric 3d human representations and learned, constrained deep translation network architectures for photographic image synthesis.
no code implementations • 23 Sep 2019 • Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Andrei Zanfir, Cristian Sminchisescu
Generating good quality and geometrically plausible synthetic images of humans with the ability to control appearance, pose and shape parameters, has become increasingly important for a variety of tasks ranging from photo editing, fashion virtual try-on, to special effects and image compression.
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 • 18 Dec 2020 • Mihai Fieraru, Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Vlad Olaru, Cristian Sminchisescu
Monocular estimation of three dimensional human self-contact is fundamental for detailed scene analysis including body language understanding and behaviour modeling.
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 • Mihai Fieraru, Mihai Zanfir, Silviu Cristian Pirlea, Vlad Olaru, Cristian Sminchisescu
AIFit is able to reconstruct 3d human pose and motion, reliably segment exercise repetitions, and identify in real-time the deviations between standards learnt from trainers, and the execution of a trainee.
no code implementations • NeurIPS 2021 • Mihai Fieraru, Mihai Zanfir, Teodor Szente, Eduard Bazavan, Vlad Olaru, Cristian Sminchisescu
We introduce a novel unified model for self- and interpenetration-collisions based on a mesh approximation computed by applying decimation operators.
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 • CVPR 2022 • Thiemo Alldieck, Mihai Zanfir, Cristian Sminchisescu
We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image.
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 • 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 • 14 Dec 2022 • Mihai Zanfir, Thiemo Alldieck, Cristian Sminchisescu
We present PhoMoH, a neural network methodology to construct generative models of photo-realistic 3D geometry and appearance of human heads including hair, beards, an oral cavity, and clothing.
no code implementations • 15 Dec 2022 • Andrei Zanfir, Mihai Zanfir, Alexander Gorban, Jingwei Ji, Yin Zhou, Dragomir Anguelov, Cristian Sminchisescu
Autonomous driving is an exciting new industry, posing important research questions.
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.
1 code implementation • 3 Aug 2023 • Mihai Fieraru, Mihai Zanfir, Elisabeta Oneata, Alin-Ionut Popa, Vlad Olaru, Cristian Sminchisescu
Understanding 3d human interactions is fundamental for fine-grained scene analysis and behavioural modeling.