Search Results for author:
Found 16 papers, 0 papers with code
VLOGGER: Multimodal Diffusion for Embodied Avatar Synthesis
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.
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.
Blendshapes GHUM: Real-time Monocular Facial Blendshape Prediction
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.
HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving
Autonomous driving is an exciting new industry, posing important research questions.
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.
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
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)
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)
Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows
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 #54 on
3D Human Pose Estimation
on 3DPW
(PA-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.
Monocular 3D Pose and Shape Estimation of Multiple People in Natural Scenes - The Importance of Multiple Scene Constraints
Human sensing has greatly benefited from recent advances in deep learning, parametric human modeling, and large scale 2d and 3d datasets.
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.
Deep Learning of Graph Matching
The problem of graph matching under node and pair-wise constraints is fundamental in areas as diverse as combinatorial optimization, machine learning or computer vision, where representing both the relations between nodes and their neighborhood structure is essential.
Ranked #18 on
Graph Matching
on Willow Object Class
Large Displacement 3D Scene Flow With Occlusion Reasoning
In this paper we propose a novel coarse to fine correspondence-based scene flow approach to account for the effects of large displacements and to model occlusion, based on explicit geometric reasoning.
