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

Convolutional Neural Networks (CNNs) are commonly thought to recognise
objects by learning increasingly complex representations of object shapes. Some
recent studies suggest a more important role of image textures. We here put
these conflicting hypotheses to a quantitative test by evaluating CNNs and
human observers on images with a texture-shape cue conflict. We show that
ImageNet-trained CNNs are strongly biased towards recognising textures rather
than shapes, which is in stark contrast to human behavioural evidence and
reveals fundamentally different classification strategies. We then demonstrate
that the same standard architecture (ResNet-50) that learns a texture-based
representation on ImageNet is able to learn a shape-based representation
instead when trained on "Stylized-ImageNet", a stylized version of ImageNet.
This provides a much better fit for human behavioural performance in our
well-controlled psychophysical lab setting (nine experiments totalling 48,560
psychophysical trials across 97 observers) and comes with a number of
unexpected emergent benefits such as improved object detection performance and
previously unseen robustness towards a wide range of image distortions,
highlighting advantages of a shape-based representation.

这篇文章通过对卷积神经网络（CNNs）和人类观察者在具有纹理 - 形状线索冲突的图像上进行实验，揭示了 CNN 在识别物体时更倾向于识别纹理而非形状的基本不同分类策略，同时表明将 CNN 在 Stylized-ImageNet 数据集上训练可以学习到形状为基础的表示，可以更好地拟合人类行为表现，同时具有提高物体检测性能和对各种图像扭曲的鲁棒性的优点。

ImageNet 训练的 CNN 偏向于纹理；增加形状偏差可以提高准确性和鲁棒性

ImageNet-trained CNNs are biased towards texture; increasing shape bias  improves accuracy and robustness

We propose a novel attention model that can accurately attends to target
objects of various scales and shapes in images. The model is trained to
gradually suppress irrelevant regions in an input image via a progressive
attentive process over multiple layers of a convolutional neural network. The
attentive process in each layer determines whether to pass or block features at
certain spatial locations for use in the subsequent layers. The proposed
progressive attention mechanism works well especially when combined with hard
attention. We further employ local contexts to incorporate neighborhood
features of each location and estimate a better attention probability map. The
experiments on synthetic and real datasets show that the proposed attention
networks outperform traditional attention methods in visual attribute
prediction tasks.

该研究提出了一种新的注意力模型，可以通过多层卷积神经网络的渐进式注意力过程精确地关注各种规模和形状的图像目标。实验证明，与传统的注意力方法相比，在视觉属性预测任务中，所提出的注意力网络效果更好。