We present a method that infers spatial arrangements and shapes of humans and
objects in a globally consistent 3D scene, all from a single image in-the-wild
captured in an uncontrolled environment. Notably, our method runs on datasets
without any scene- or object-level 3D supervision. Our key insight is that
considering humans and objects jointly gives rise to "3D common sense"
constraints that can be used to resolve ambiguity. In particular, we introduce
a scale loss that learns the distribution of object size from data; an
occlusion-aware silhouette re-projection loss to optimize object pose; and a
human-object interaction loss to capture the spatial layout of objects with
which humans interact. We empirically validate that our constraints
dramatically reduce the space of likely 3D spatial configurations. We
demonstrate our approach on challenging, in-the-wild images of humans
interacting with large objects (such as bicycles, motorcycles, and surfboards)
and handheld objects (such as laptops, tennis rackets, and skateboards). We
quantify the ability of our approach to recover human-object arrangements and
outline remaining challenges in this relatively domain. The project webpage can
be found at this https URL

本文提出了一种在非受控环境下从野外拍摄的单张图像中推断人类和物体的空间布局和形状的方法，并证明了尝试联合考虑人类和物体可以通过引入 3D 常识约束来减少可能的 3D 空间配置。

从单幅图像中感知野外 3D 人体 - 物体空间排列

Perceiving 3D Human-Object Spatial Arrangements from a Single Image in  the Wild

In this paper, we propose 3D point-capsule networks, an auto-encoder designed
to process sparse 3D point clouds while preserving spatial arrangements of the
input data. 3D capsule networks arise as a direct consequence of our novel
unified 3D auto-encoder formulation. Their dynamic routing scheme and the
peculiar 2D latent space deployed by our approach bring in improvements for
several common point cloud-related tasks, such as object classification, object
reconstruction and part segmentation as substantiated by our extensive
evaluations. Moreover, it enables new applications such as part interpolation
and replacement.

此论文提出了 3D 点胶囊网络作为自动编码器的一个改进，它能处理稀疏的三维点云数据并保留其空间结构。动态路由和其独特的二维潜在空间使其改进了几个与点云相关的常见任务，如物体分类、物体重建和部分分割，并能使其实现部分插值和替换。