Search Results for author:
Found 18 papers, 6 papers with code
Unite the People: Closing the Loop Between 3D and 2D Human Representations
With a comprehensive set of experiments, we show how this data can be used to train discriminative models that produce results with an unprecedented level of detail: our models predict 31 segments and 91 landmark locations on the body.
Ranked #1 on
Monocular 3D Human Pose Estimation
on Human3.6M
(Use Video Sequence metric)
3D human pose and shape estimation
Monocular 3D Human Pose Estimation
HoloLens 2 Research Mode as a Tool for Computer Vision Research
Mixed reality headsets, such as the Microsoft HoloLens 2, are powerful sensing devices with integrated compute capabilities, which makes it an ideal platform for computer vision research.
Learning Motion Priors for 4D Human Body Capture in 3D Scenes
To prove the effectiveness of the proposed motion priors, we combine them into a novel pipeline for 4D human body capture in 3D scenes.
EgoBody: Human Body Shape and Motion of Interacting People from Head-Mounted Devices
Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view.
Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints.
Ranked #29 on
3D Human Pose Estimation
on HumanEva-I
H+O: Unified Egocentric Recognition of 3D Hand-Object Poses and Interactions
Given a single RGB image, our model jointly estimates the 3D hand and object poses, models their interactions, and recognizes the object and action classes with a single feed-forward pass through a neural network.
Towards Accurate Markerless Human Shape and Pose Estimation over Time
Existing marker-less motion capture methods often assume known backgrounds, static cameras, and sequence specific motion priors, which narrows its application scenarios.
FAUST: Dataset and Evaluation for 3D Mesh Registration
We address this with a novel mesh registration technique that combines 3D shape and appearance information to produce high-quality alignments.
Dynamic FAUST: Registering Human Bodies in Motion
We propose a new mesh registration method that uses both 3D geometry and texture information to register all scans in a sequence to a common reference topology.
Detailed Full-Body Reconstructions of Moving People From Monocular RGB-D Sequences
The method then uses geometry and image texture over time to obtain accurate shape, pose, and appearance information despite unconstrained motion, partial views, varying resolution, occlusion, and soft tissue deformation.
Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition
The lack of large-scale real datasets with annotations makes transfer learning a necessity for video activity understanding.
Leveraging Photometric Consistency over Time for Sparsely Supervised Hand-Object Reconstruction
Modeling hand-object manipulations is essential for understanding how humans interact with their environment.
Ranked #9 on
hand-object pose
on HO-3D
The Phong Surface: Efficient 3D Model Fitting using Lifted Optimization
Realtime perceptual and interaction capabilities in mixed reality require a range of 3D tracking problems to be solved at low latency on resource-constrained hardware such as head-mounted devices.
H2O: Two Hands Manipulating Objects for First Person Interaction Recognition
To this end, we propose a method to create a unified dataset for egocentric 3D interaction recognition.
Learning to Fit Morphable Models
A common first step in systems that tackle these problems is to regress the parameters of the parametric model directly from the input data.
Spatial Computing and Intuitive Interaction: Bringing Mixed Reality and Robotics Together
Spatial computing -- the ability of devices to be aware of their surroundings and to represent this digitally -- offers novel capabilities in human-robot interaction.
FLAG: Flow-based 3D Avatar Generation from Sparse Observations
To represent people in mixed reality applications for collaboration and communication, we need to generate realistic and faithful avatar poses.
RoHM: Robust Human Motion Reconstruction via Diffusion
We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling.
