Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …

In this paper, we are interested in the bottom-up paradigm of estimating human poses from
an image. We study the dense keypoint regression framework that is previously inferior to …

Vanilla convolutions in modern deep networks are known to operate locally and at fixed
scale (eg, the widely-adopted 3* 3 kernels in image-oriented tasks). This causes low efficacy …

Human pose is typically represented by a coordinate vector of body joints or their heatmap
embeddings. While easy for data processing, unrealistic pose estimates are admitted due to …

While being the de facto standard coordinate representation for human pose estimation,
heatmap has not been investigated in-depth. This work fills this gap. For the first time, we …

We present a statistical, articulated 3D human shape modeling pipeline, within a fully
trainable, modular, deep learning framework. Given high-resolution complete 3D body …

Frame reconstruction (current or future frames) based on Auto-Encoder (AE) is a popular
method for video anomaly detection. With models trained on the normal data, the …

Multi-frame human pose estimation in complicated situations is challenging. Although state-
of-the-art human joints detectors have demonstrated remarkable results for static images …

In this paper, we propose a novel method called Residual Steps Network (RSN). RSN
aggregates features with the same spatial size (Intra-level features) efficiently to obtain …