Search Results for author:
Found 19 papers, 1 papers with code
SPHEAR: Spherical Head Registration for Complete Statistical 3D Modeling
We present \emph{SPHEAR}, an accurate, differentiable parametric statistical 3D human head model, enabled by a novel 3D registration method based on spherical embeddings.
Reconstructing Three-Dimensional Models of Interacting Humans
Understanding 3d human interactions is fundamental for fine-grained scene analysis and behavioural modeling.
HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving
Autonomous driving is an exciting new industry, posing important research questions.
PhoMoH: Implicit Photorealistic 3D Models of Human Heads
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.
Structured 3D Features for Reconstructing Controllable Avatars
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.
BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation
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.
Photorealistic Monocular 3D Reconstruction of Humans Wearing Clothing
We present PHORHUM, a novel, end-to-end trainable, deep neural network methodology for photorealistic 3D human reconstruction given just a monocular RGB image.
HSPACE: Synthetic Parametric Humans Animated in Complex Environments
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
REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision
We introduce a novel unified model for self- and interpenetration-collisions based on a mesh approximation computed by applying decimation operators.
AIFit: Automatic 3D Human-Interpretable Feedback Models for Fitness Training
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.
THUNDR: Transformer-based 3D HUmaN Reconstruction with Markers
We present THUNDR, a transformer-based deep neural network methodology to reconstruct the 3d pose and shape of people, given monocular RGB images.
Ranked #41 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
Learning Complex 3D Human Self-Contact
Monocular estimation of three dimensional human self-contact is fundamental for detailed scene analysis including body language understanding and behaviour modeling.
Neural Descent for Visual 3D Human Pose and Shape
We present deep neural network methodology to reconstruct the 3d pose and shape of people, given an input RGB image.
Ranked #62 on
3D Human Pose Estimation
on 3DPW
(MPJPE metric)
Human Synthesis and Scene Compositing
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.
Deep Network for the Integrated 3D Sensing of Multiple People in Natural Images
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.
Human Appearance Transfer
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.
3D Human Sensing, Action and Emotion Recognition in Robot Assisted Therapy of Children With Autism
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.
Deep Multitask Architecture for Integrated 2D and 3D Human Sensing
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
Spatio-Temporal Attention Models for Grounded Video Captioning
Automatic video captioning is challenging due to the complex interactions in dynamic real scenes.
