Human motion forecasting, with the goal of estimating future human behavior
over a period of time, is a fundamental task in many real-world applications.
However, existing works typically concentrate on predicting the major joints of
the human body without considering the delicate movements of the human hands.
In practical applications, hand gesture plays an important role in human
communication with the real world, and expresses the primary intention of human
beings. In this work, we are the first to formulate a whole-body human pose
forecasting task, which jointly predicts the future body and hand activities.
Correspondingly, we propose a novel Encoding-Alignment-Interaction (EAI)
framework that aims to predict both coarse (body joints) and fine-grained
(gestures) activities collaboratively, enabling expressive and
cross-facilitated forecasting of 3D whole-body human motions. Specifically, our
model involves two key constituents: cross-context alignment (XCA) and
cross-context interaction (XCI). Considering the heterogeneous information
within the whole-body, XCA aims to align the latent features of various human
components, while XCI focuses on effectively capturing the context interaction
among the human components. We conduct extensive experiments on a
newly-introduced large-scale benchmark and achieve state-of-the-art
performance. The code is public for research purposes at
this https URL

通过 Encoding-Alignment-Interaction（EAI）框架提出了一种全身人体姿势预测任务，能够协同预测未来的身体和手部活动，从而实现对三维全身人体动作的表达性和跨领域的预测。

3D 全身人体动作的表达性预测

Expressive Forecasting of 3D Whole-body Human Motions

Hand gesture-based human-computer interaction is an important problem that is
well explored using color camera data. In this work we proposed a hand gesture
detection system using thermal images. Our system is capable of handling
multiple hand regions in a frame and process it fast for real-time
applications. Our system performs a series of steps including background
subtraction-based hand mask generation, k-means based hand region
identification, hand segmentation to remove the forearm region, and a
Convolutional Neural Network (CNN) based gesture classification. Our work
introduces two novel algorithms, bubble growth and bubble search, for faster
hand segmentation. We collected a new thermal image data set with 10 gestures
and reported an end-to-end hand gesture recognition accuracy of 97%.

本文展示了一种使用热像数据的手势检测系统，使用基于背景减法的手掩模生成、k-means 算法手区域识别、手臂区域去除的手势分割以及基于卷积神经网络的手势分类来实现多个手区域的快速处理， 并引入了泡沫生长和泡沫搜索两种新算法， 进行更快的手势分割。我们收集了一个包含 10 种手势的新热像数据集，并报道了 97％的端到端手势识别准确率。

热像实时手势识别

Real-Time Hand Gesture Identification in Thermal Images

Hand gesture-to-gesture translation in the wild is a challenging task since
hand gestures can have arbitrary poses, sizes, locations and self-occlusions.
Therefore, this task requires a high-level understanding of the mapping between
the input source gesture and the output target gesture. To tackle this problem,
we propose a novel hand Gesture Generative Adversarial Network (GestureGAN).
GestureGAN consists of a single generator $G$ and a discriminator $D$, which
takes as input a conditional hand image and a target hand skeleton image.
GestureGAN utilizes the hand skeleton information explicitly, and learns the
gesture-to-gesture mapping through two novel losses, the color loss and the
cycle-consistency loss. The proposed color loss handles the issue of "channel
pollution" while back-propagating the gradients. In addition, we present the
Fr\'echet ResNet Distance (FRD) to evaluate the quality of generated images.
Extensive experiments on two widely used benchmark datasets demonstrate that
the proposed GestureGAN achieves state-of-the-art performance on the
unconstrained hand gesture-to-gesture translation task. Meanwhile, the
generated images are in high-quality and are photo-realistic, allowing them to
be used as data augmentation to improve the performance of a hand gesture
classifier. Our model and code are available at
this https URL

提出了一种新的手势生成对抗网络 (GestureGAN) ，通过显式地利用手部骨骼信息和两个新的 loss 函数：color loss 和 cycle-consistency loss 来实现手势翻译任务。在两个广泛使用的基准数据集上进行的大量实验表明，提出的 GestureGAN 在非约束性手势翻译任务中实现了最先进的性能，并生成高质量、逼真的图像，可用作改进手势分类器性能的数据增强。