74 papers with code • 7 benchmarks • 5 datasets
Keypoint detection involves simultaneously detecting people and localizing their keypoints. Keypoints are the same thing as interest points. They are spatial locations, or points in the image that define what is interesting or what stand out in the image. They are invariant to image rotation, shrinkage, translation, distortion, and so on.
( Image credit: PifPaf: Composite Fields for Human Pose Estimation; "Learning to surf" by fotologic, license: CC-BY-2.0 )
The goal of this paper is to advance the state-of-the-art of articulated pose estimation in scenes with multiple people.
Ranked #1 on Multi-Person Pose Estimation on WAF
We introduce associative embedding, a novel method for supervising convolutional neural networks for the task of detection and grouping.
Ranked #5 on Keypoint Detection on MPII Multi-Person
First, based on our observation that the probability density of the output space is sparse, we introduce a spatial probability distribution to describe this sparsity and then use it to guide the learning of the adversarial regressor.
Existing pose estimation approaches fall into two categories: single-stage and multi-stage methods.
Ranked #1 on Pose Estimation on COCO minival
In this paper, we present a novel network structure called Cascaded Pyramid Network (CPN) which targets to relieve the problem from these "hard" keypoints.
Ranked #3 on Multi-Person Pose Estimation on COCO
We present a generic neural network architecture that uses Composite Fields to detect and construct a spatio-temporal pose which is a single, connected graph whose nodes are the semantic keypoints (e. g., a person's body joints) in multiple frames.
Ranked #4 on Multi-Person Pose Estimation on COCO