We propose Human Pose Models that represent RGB and depth images of human
poses independent of clothing textures, backgrounds, lighting conditions, body
shapes and camera viewpoints. Learning such universal models requires training
images where all factors are varied for every human pose. Capturing such data
is prohibitively expensive. Therefore, we develop a framework for synthesizing
the training data. First, we learn representative human poses from a large
corpus of real motion captured human skeleton data. Next, we fit synthetic 3D
humans with different body shapes to each pose and render each from 180 camera
viewpoints while randomly varying the clothing textures, background and
lighting. Generative Adversarial Networks are employed to minimize the gap
between synthetic and real image distributions. CNN models are then learned
that transfer human poses to a shared high-level invariant space. The learned
CNN models are then used as invariant feature extractors from real RGB and
depth frames of human action videos and the temporal variations are modelled by
Fourier Temporal Pyramid. Finally, linear SVM is used for classification.
Experiments on three benchmark cross-view human action datasets show that our
algorithm outperforms existing methods by significant margins for RGB only and
RGB-D action recognition.

我们提出了一种人体姿态模型，可表示与服装纹理、背景、光线条件、身体形状和摄像机视点无关的 RGB 和深度图像。通过开发一个综合训练数据的框架，我们学习了 CNN 模型并使用它们从真正的 RGB 和深度帧的人体动作视频中提取不变特征。在三个基准跨视图人体动作数据集的实验中，我们的算法在 RGB 和 RGB-D 动作识别方面明显优于现有方法。