We live in a 3D world where people interact with each other in the environment. Learning 3D posed humans therefore requires us to perceive and interpret these interactions. This paper proposes LEAPSE, a novel method that learns salient instance affordances for estimating a posed body from a single RGB image in a non-parametric manner. Existing methods mostly ignore the environment and estimate the human body independently from the surroundings. We capture the influences of non-contact and contact instances on a posed body as an adequate representation of the “environment affordances”. The proposed method learns the global relationships between 3D joints, body mesh vertices, and salient instances as environment affordances on the human body. LEAPSE achieved state-of-the-art results on the 3DPW dataset with many affordance instances, and also demonstrated excellent performance on Human3.6M dataset. We further demonstrate the benefit of our method by showing that the performance of existing weak models can be significantly improved when combined with our environment affordance module.

中文翻译：

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LEAPSE：3D 人体姿势和形状估计的学习环境功能可供性

我们生活在一个 3D 世界中，人们在环境中相互交互。因此，学习 3D 姿势的人类需要我们感知和解释这些交互。本文提出了 LEAPSE，这是一种学习显着实例可供性的新方法，用于以非参数方式从单个 RGB 图像中估计姿势身体。现有的方法大多忽略环境，独立于周围环境来估计人体。我们捕捉非接触和接触实例对姿势身体的影响，作为“环境可供性”的充分表示。所提出的方法学习 3D 关节、身体网格顶点和显着实例之间的全局关系作为人体的环境可供性。 LEAPSE 在具有许多可供性实例的 3DPW 数据集上取得了最先进的结果，并且还在 Human3.6M 数据集上展示了出色的性能。我们进一步证明了我们的方法的好处，表明与我们的环境可供性模块相结合时，现有弱模型的性能可以得到显着提高。