Abstract This paper presents a part-based face detection approach where the spatial relationship between the face parts is represented by a hidden 3D model with six parameters. The computational complexity of the search in the six dimensional pose s
Action recognition and human pose estimation are closely related but both problems are generally handled as distinct tasks in the literature. In this work, we pro- pose a multitask framework for jointly 2D and 3D pose estimation from still images an
Recently, remarkable advances have been achieved in 3D human pose estimation from monocular images because of the powerful Deep Convolutional Neural Networks (DC- NNs). Despite their success on large-scale datasets col- lected in the constrained lab
We propose a scalable, efficient and accurate approach to retrieve 3D models for objects in the wild. Our contri- bution is twofold. We first present a 3D pose estimation approach for object categories which significantly outper- forms the state-of-
We present a fast inverse-graphics framework for instance-level 3D scene understanding. We train a deep convolutional network that learns to map image regions to the full 3D shape and pose of all object instances in the image. Our method produces a
Learning a Deep Network with Spherical Part Model for 3D Hand Pose EstimationImage feature extraction sub-network!F
mage embedding
I pose prediction auxiliary task
3112×112
2x526424x246410X101
1024
51
fpl
2048
1024
256
lII
3d pose
firer
0.9
dot-produ
3D Human Pose Estimation = 2D Pose Estimation + Matching(2017)
Abstract
Many approaches try to directly predict 3D pose from image measurements,we explore a simple architecture that reasons through intermediate 2D pose directions.
（1）Deep neural nets
Real-time 3D Pose Estimation with a Monocular Camera Using Deep Learning and Object Priors On an Autonomous Racecar
背景
三维物体投影在平面上会失去一个维度，即不知道物体的距离。但是，有了三维物体的先验信息，我们可以知道三维物体的距离
To this end, we propose a low-latency real-time pipeline to detect and est
Anipose
Anipose is an open-source toolkit for robust, markerless 3D pose estimation of animal behavior from multiple camera views. It leverages the machine learning toolbox to track keypoints in 2D, then triangulates across camera views to estimate
