33,821 research outputs found
PifPaf: Composite Fields for Human Pose Estimation
We propose a new bottom-up method for multi-person 2D human pose estimation
that is particularly well suited for urban mobility such as self-driving cars
and delivery robots. The new method, PifPaf, uses a Part Intensity Field (PIF)
to localize body parts and a Part Association Field (PAF) to associate body
parts with each other to form full human poses. Our method outperforms previous
methods at low resolution and in crowded, cluttered and occluded scenes thanks
to (i) our new composite field PAF encoding fine-grained information and (ii)
the choice of Laplace loss for regressions which incorporates a notion of
uncertainty. Our architecture is based on a fully convolutional, single-shot,
box-free design. We perform on par with the existing state-of-the-art bottom-up
method on the standard COCO keypoint task and produce state-of-the-art results
on a modified COCO keypoint task for the transportation domain.Comment: CVPR 201
Self Adversarial Training for Human Pose Estimation
This paper presents a deep learning based approach to the problem of human
pose estimation. We employ generative adversarial networks as our learning
paradigm in which we set up two stacked hourglass networks with the same
architecture, one as the generator and the other as the discriminator. The
generator is used as a human pose estimator after the training is done. The
discriminator distinguishes ground-truth heatmaps from generated ones, and
back-propagates the adversarial loss to the generator. This process enables the
generator to learn plausible human body configurations and is shown to be
useful for improving the prediction accuracy.Comment: CVPR 2017 Workshop on Visual Understanding of Humans in Crowd Scene
and the 1st Look Into Person (LIP) Challeng
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