We present a simple and effective method for 3D hand pose estimation from a
single depth frame. As opposed to previous state-of-the-art methods based on
holistic 3D regression, our method works on dense pixel-wise estimation. This
is achieved by careful design choices in pose parameterization, which leverages
both 2D and 3D properties of depth map. Specifically, we decompose the pose
parameters into a set of per-pixel estimations, i.e., 2D heat maps, 3D heat
maps and unit 3D directional vector fields. The 2D/3D joint heat maps and 3D
joint offsets are estimated via multi-task network cascades, which is trained
end-to-end. The pixel-wise estimations can be directly translated into a vote
casting scheme. A variant of mean shift is then used to aggregate local votes
while enforcing consensus between the the estimated 3D pose and the pixel-wise
2D and 3D estimations by design. Our method is efficient and highly accurate.
On MSRA and NYU hand dataset, our method outperforms all previous
state-of-the-art approaches by a large margin. On the ICVL hand dataset, our
method achieves similar accuracy compared to the currently proposed nearly
saturated result and outperforms various other proposed methods. Code is
available $\href{"this https URL"}{\text{online}}$.

该研究提出了一种基于单个深度帧的三维手部姿态估计方法，通过像素级别的估计实现高效准确地实现骨架姿态估计，并在 MSRA 和 NYU 手部数据集上，其方法实现了最好的性能。

手势姿势估计的密集三维回归

Dense 3D Regression for Hand Pose Estimation

Semantic segmentation research has recently witnessed rapid progress, but
many leading methods are unable to identify object instances. In this paper, we
present Multi-task Network Cascades for instance-aware semantic segmentation.
Our model consists of three networks, respectively differentiating instances,
estimating masks, and categorizing objects. These networks form a cascaded
structure, and are designed to share their convolutional features. We develop
an algorithm for the nontrivial end-to-end training of this causal, cascaded
structure. Our solution is a clean, single-step training framework and can be
generalized to cascades that have more stages. We demonstrate state-of-the-art
instance-aware semantic segmentation accuracy on PASCAL VOC. Meanwhile, our
method takes only 360ms testing an image using VGG-16, which is two orders of
magnitude faster than previous systems for this challenging problem. As a by
product, our method also achieves compelling object detection results which
surpass the competitive Fast/Faster R-CNN systems.
The method described in this paper is the foundation of our submissions to
the MS COCO 2015 segmentation competition, where we won the 1st place.

本文介绍了一种基于 Multi-task Network Cascades 的实例感知语义分割模型，该模型包含三个网络，分别用于区分实例、估算掩模和分类物体。该方法在 PASCAL VOC 数据集上达到了最先进的实例感知语义分割准确度，并且速度比之前的系统快两个数量级。