We introduce a pipeline to address anatomical inaccuracies in Stable
Diffusion generated hand images. The initial step involves constructing a
specialized dataset, focusing on hand anomalies, to train our models
effectively. A finetuned detection model is pivotal for precise identification
of these anomalies, ensuring targeted correction. Body pose estimation aids in
understanding hand orientation and positioning, crucial for accurate anomaly
correction. The integration of ControlNet and InstructPix2Pix facilitates
sophisticated inpainting and pixel-level transformation, respectively. This
dual approach allows for high-fidelity image adjustments. This comprehensive
approach ensures the generation of images with anatomically accurate hands,
closely resembling real-world appearances. Our experimental results demonstrate
the pipeline's efficacy in enhancing hand image realism in Stable Diffusion
outputs. We provide an online demo at this https URL

我们介绍了一种处理稳定扩散生成的手部图像中解剖学不准确性的流程。我们首先构建了一个专门针对手部异常的数据集，以有效训练我们的模型。一个经过微调的检测模型对这些异常的精确定位至关重要，确保了有针对性的修正。身体姿势估计有助于理解手部的方向和位置，对精确的异常修正非常重要。ControlNet 和 InstructPix2Pix 的集成分别实现了复杂的修复和像素级变换。这种双重方法可以进行高保真度的图像调整。这种全面的方法确保生成的图像具有解剖学准确的手部，与真实世界的外观非常接近。我们的实验结果证明了该流程在增强 Stable Diffusion 输出中手部图像逼真度方面的有效性。我们在此 https 网址提供了在线演示。

稳定扩散生成图像中非标准手势的检测与修复

Detecting and Restoring Non-Standard Hands in Stable Diffusion Generated  Images

We present MovePose, an optimized lightweight convolutional neural network
designed specifically for real-time body pose estimation on CPU-based mobile
devices. The current solutions do not provide satisfactory accuracy and speed
for human posture estimation, and MovePose addresses this gap. It aims to
maintain real-time performance while improving the accuracy of human posture
estimation for mobile devices. The network produces 17 keypoints for each
individual at a rate exceeding 11 frames per second, making it suitable for
real-time applications such as fitness tracking, sign language interpretation,
and advanced mobile human posture estimation. Our MovePose algorithm has
attained an Mean Average Precision (mAP) score of 67.7 on the COCO
\cite{cocodata} validation dataset. The MovePose algorithm displayed efficiency
with a performance of 69+ frames per second (fps) when run on an Intel
i9-10920x CPU. Additionally, it showcased an increased performance of 452+ fps
on an NVIDIA RTX3090 GPU. On an Android phone equipped with a Snapdragon 8 + 4G
processor, the fps reached above 11. To enhance accuracy, we incorporated three
techniques: deconvolution, large kernel convolution, and coordinate
classification methods. Compared to basic upsampling, deconvolution is
trainable, improves model capacity, and enhances the receptive field. Large
kernel convolution strengthens these properties at a decreased computational
cost. In summary, MovePose provides high accuracy and real-time performance,
marking it a potential tool for a variety of applications, including those
focused on mobile-side human posture estimation. The code and models for this
algorithm will be made publicly accessible.

MovePose 是一种为 CPU-based 移动设备上的实时人体姿势估计而设计的优化轻量级卷积神经网络，具有高精度和实时性能。

MovePose: 移动和边缘设备上的高性能人体姿势估计算法

MovePose: A High-performance Human Pose Estimation Algorithm on Mobile  and Edge Devices

Understanding the camera wearer's activity is central to egocentric vision,
yet one key facet of that activity is inherently invisible to the camera--the
wearer's body pose. Prior work focuses on estimating the pose of hands and arms
when they come into view, but this 1) gives an incomplete view of the full body
posture, and 2) prevents any pose estimate at all in many frames, since the
hands are only visible in a fraction of daily life activities. We propose to
infer the "invisible pose" of a person behind the egocentric camera. Given a
single video, our efficient learning-based approach returns the full body 3D
joint positions for each frame. Our method exploits cues from the dynamic
motion signatures of the surrounding scene--which changes predictably as a
function of body pose--as well as static scene structures that reveal the
viewpoint (e.g., sitting vs. standing). We further introduce a novel energy
minimization scheme to infer the pose sequence. It uses soft predictions of the
poses per time instant together with a non-parametric model of human pose
dynamics over longer windows. Our method outperforms an array of possible
alternatives, including deep learning approaches for direct pose regression
from images.

通过利用环境的动态运动和静态构造特征，以及能够推断身体姿态序列的新型最小化能量模型，我们提出了一种高效的基于学习的方法来推断佩戴 egocentric 相机的人体姿态的 3D 关节位置，进一步改进了基于深度学习的图像直接姿态回归等可能的替代方案。