11,569 research outputs found
Weakly-supervised Pre-training for 3D Human Pose Estimation via Perspective Knowledge
Modern deep learning-based 3D pose estimation approaches require plenty of 3D
pose annotations. However, existing 3D datasets lack diversity, which limits
the performance of current methods and their generalization ability. Although
existing methods utilize 2D pose annotations to help 3D pose estimation, they
mainly focus on extracting 2D structural constraints from 2D poses, ignoring
the 3D information hidden in the images. In this paper, we propose a novel
method to extract weak 3D information directly from 2D images without 3D pose
supervision. Firstly, we utilize 2D pose annotations and perspective prior
knowledge to generate the relationship of that keypoint is closer or farther
from the camera, called relative depth. We collect a 2D pose dataset (MCPC) and
generate relative depth labels. Based on MCPC, we propose a weakly-supervised
pre-training (WSP) strategy to distinguish the depth relationship between two
points in an image. WSP enables the learning of the relative depth of two
keypoints on lots of in-the-wild images, which is more capable of predicting
depth and generalization ability for 3D human pose estimation. After
fine-tuning on 3D pose datasets, WSP achieves state-of-the-art results on two
widely-used benchmarks
The relationship of electron Fermi energy with strong magnetic fields
In order to depict the quantization of Landau levels, we introduce Dirac
function, and gain a concise expression for the electron Fermi energy,
. The high soft X-ray luminosities of magnetars may
be naturally explained by our theory.Comment: 3 pages, 1 figure, submitted to OMEG11 Proceeding (Tokyo, Japan.
Nov.14-18, 2011
- …