Search Results for author:
Found 18 papers, 8 papers with code
Gravity-Aware Monocular 3D Human-Object Reconstruction
We evaluate GraviCap on a new dataset with ground-truth annotations for persons and different objects undergoing free flights.
Multi-Person 3D Human Pose Estimation from Monocular Images
Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data.
Ranked #7 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Pose Estimation
3D Multi-Person Human Pose Estimation
ProtoGAN: Towards Few Shot Learning for Action Recognition
To the best of our knowledge, we are the first to report the results for G-FSL and provide a strong benchmark for future research.
Progression Modelling for Online and Early Gesture Detection
Online and Early detection of gestures is crucial for building touchless gesture based interfaces.
On the Robustness of Human Pose Estimation
This paper provides a comprehensive and exhaustive study of adversarial attacks on human pose estimation models and the evaluation of their robustness.
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation
The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire.
Monocular 3D Human Pose Estimation by Generation and Ordinal Ranking
Monocular 3D human-pose estimation from static images is a challenging problem, due to the curse of dimensionality and the ill-posed nature of lifting 2D-to-3D.
Removal of Batch Effects using Generative Adversarial Networks
These variations are known as Batch Effects.
An Improved Learning Framework for Covariant Local Feature Detection
However, just learning from covariant constraint can lead to detection of unstable features.
A Large Dataset for Improving Patch Matching
Similarly on the Strecha dataset, we see an improvement of 3-5% for the matching task in non-planar scenes.
Learning 3D Human Pose from Structure and Motion
3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data.
Ranked #19 on
Monocular 3D Human Pose Estimation
on Human3.6M
Monocular 3D Human Pose Estimation
Weakly-supervised Learning
Improved Descriptors for Patch Matching and Reconstruction
Scenes from the Oxford ACRD, MVS and Synthetic datasets are used for evaluating the patch matching performance of the learnt descriptors while the Strecha dataset is used to evaluate the 3D reconstruction task.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Efficient ConvNet-Based Marker-Less Motion Capture in General Scenes With a Low Number of Cameras
Our approach unites a discriminative image-based joint detection method with a model-based generative motion tracking algorithm through a combined pose optimization energy.
Efficient Object Localization Using Convolutional Networks
Recent state-of-the-art performance on human-body pose estimation has been achieved with Deep Convolutional Networks (ConvNets).
Ranked #42 on
Pose Estimation
on MPII Human Pose
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features.
Joint Training of a Convolutional Network and a Graphical Model for Human Pose Estimation
This paper proposes a new hybrid architecture that consists of a deep Convolutional Network and a Markov Random Field.
Learning Human Pose Estimation Features with Convolutional Networks
This paper introduces a new architecture for human pose estimation using a multi- layer convolutional network architecture and a modified learning technique that learns low-level features and higher-level weak spatial models.
