4,773 research outputs found
Feature Mapping for Learning Fast and Accurate 3D Pose Inference from Synthetic Images
We propose a simple and efficient method for exploiting synthetic images when
training a Deep Network to predict a 3D pose from an image. The ability of
using synthetic images for training a Deep Network is extremely valuable as it
is easy to create a virtually infinite training set made of such images, while
capturing and annotating real images can be very cumbersome. However, synthetic
images do not resemble real images exactly, and using them for training can
result in suboptimal performance. It was recently shown that for exemplar-based
approaches, it is possible to learn a mapping from the exemplar representations
of real images to the exemplar representations of synthetic images. In this
paper, we show that this approach is more general, and that a network can also
be applied after the mapping to infer a 3D pose: At run time, given a real
image of the target object, we first compute the features for the image, map
them to the feature space of synthetic images, and finally use the resulting
features as input to another network which predicts the 3D pose. Since this
network can be trained very effectively by using synthetic images, it performs
very well in practice, and inference is faster and more accurate than with an
exemplar-based approach. We demonstrate our approach on the LINEMOD dataset for
3D object pose estimation from color images, and the NYU dataset for 3D hand
pose estimation from depth maps. We show that it allows us to outperform the
state-of-the-art on both datasets.Comment: CVPR 201
Unsupervised Human Action Recognition with Skeletal Graph Laplacian and Self-Supervised Viewpoints Invariance
This paper presents a novel end-to-end method for the problem of
skeleton-based unsupervised human action recognition. We propose a new
architecture with a convolutional autoencoder that uses graph Laplacian
regularization to model the skeletal geometry across the temporal dynamics of
actions. Our approach is robust towards viewpoint variations by including a
self-supervised gradient reverse layer that ensures generalization across
camera views. The proposed method is validated on NTU-60 and NTU-120
large-scale datasets in which it outperforms all prior unsupervised
skeleton-based approaches on the cross-subject, cross-view, and cross-setup
protocols. Although unsupervised, our learnable representation allows our
method even to surpass a few supervised skeleton-based action recognition
methods. The code is available in:
www.github.com/IIT-PAVIS/UHAR_Skeletal_Laplacia
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