In this paper, we are interested in the human pose estimation problem with a focus on
learning reliable high-resolution representations. Most existing methods recover high …

Quantitative behavioral measurements are important for answering questions across
scientific disciplines—from neuroscience to ecology. State-of-the-art deep-learning methods …

Existing pose estimation approaches fall into two categories: single-stage and multi-stage
methods. While multi-stage methods are seemingly more suited for the task, their …

Multi-person pose estimation from a 2D image is an essential technique for human behavior
understanding. In this paper, we propose a human pose refinement network that estimates a …

Estimating 3D human pose from a single image is a challenging task. This work attempts to
address the uncertainty of lifting the detected 2D joints to the 3D space by introducing an …

Multi-scale context module and single-stage encoder-decoder structure are commonly
employed for semantic segmentation. The multi-scale context module refers to the …

Recent years have witnessed an unprecedented growing of sport videos, as different types
of sports activities can be widely-observed (ie, from professional athletics to personal …

We present the first single-network approach for 2D whole-body pose estimation, which
entails simultaneous localization of body, face, hands, and feet keypoints. Due to the bottom …

Recent research on human pose estimation has achieved significant improvement.
However, most existing methods tend to pursue higher scores using complex architecture or …

Human pose estimation (HPE) is inherently a homogeneous multi-task learning problem,
with the localization of each body part as a different task. Recent HPE approaches …