The interaction decoder utilized in prevalent Transformer-based HOI detectors
typically accepts pre-composed human-object pairs as inputs. Though achieving
remarkable performance, such paradigm lacks feasibility and cannot explore
novel combinations over entities during decoding. We present L OGIC HOI, a new
HOI detector that leverages neural-logic reasoning and Transformer to infer
feasible interactions between entities. Specifically, we modify the
self-attention mechanism in vanilla Transformer, enabling it to reason over the
<human, action, object> triplet and constitute novel interactions. Meanwhile,
such reasoning process is guided by two crucial properties for understanding
HOI: affordances (the potential actions an object can facilitate) and proxemics
(the spatial relations between humans and objects). We formulate these two
properties in first-order logic and ground them into continuous space to
constrain the learning process of our approach, leading to improved performance
and zero-shot generalization capabilities. We evaluate L OGIC HOI on V-COCO and
HICO-DET under both normal and zero-shot setups, achieving significant
improvements over existing methods.

使用神经逻辑推理和 Transformer 来推断实体之间的可行互动，通过改进 Transformer 的自注意机制，使用逻辑约束学习过程，提高性能和零样本泛化能力。

神经逻辑人 - 物体交互检测

Neural-Logic Human-Object Interaction Detection

Social interaction is a fundamental aspect of human behavior and
communication. The way individuals position themselves in relation to others,
also known as proxemics, conveys social cues and affects the dynamics of social
interaction. We present a novel approach that learns a 3D proxemics prior of
two people in close social interaction. Since collecting a large 3D dataset of
interacting people is a challenge, we rely on 2D image collections where social
interactions are abundant. We achieve this by reconstructing pseudo-ground
truth 3D meshes of interacting people from images with an optimization approach
using existing ground-truth contact maps. We then model the proxemics using a
novel denoising diffusion model called BUDDI that learns the joint distribution
of two people in close social interaction directly in the SMPL-X parameter
space. Sampling from our generative proxemics model produces realistic 3D human
interactions, which we validate through a user study. Additionally, we
introduce a new optimization method that uses the diffusion prior to
reconstruct two people in close proximity from a single image without any
contact annotation. Our approach recovers more accurate and plausible 3D social
interactions from noisy initial estimates and outperforms state-of-the-art
methods. See our project site for code, data, and model:
muelea.github.io/buddi.

本文通过一种基于 2D 图像的方法学习了二人社交互动的 3D proxemics 数据，介绍了一种新的去噪扩散模型 BUDDI 来可视化 3D 人际互动情境，并利用新的优化方法从单张图像中重构两个人的社交关系，其结果相较于现有方法更为准确和合理。

生成接近感：从图像中的三维社交互动的先验

Generative Proxemics: A Prior for 3D Social Interaction from Images

Conventional automatic doors cannot distinguish between people wishing to
pass through the door and people passing by the door, so they often open
unnecessarily. This leads to the need to adopt new systems in both commercial
and non-commercial environments: smart doors. In particular, a smart door
system predicts the intention of people near the door based on the social
context of the surrounding environment and then makes rational decisions about
whether or not to open the door. This work proposes the first position paper
related to smart doors, without bells and whistles. We first point out that the
problem not only concerns reliability, climate control, safety, and mode of
operation. Indeed, a system to predict the intention of people near the door
also involves a deeper understanding of the social context of the scene through
a complex combined analysis of proxemics and scene reasoning. Furthermore, we
conduct an exhaustive literature review about automatic doors, providing a
novel system formulation. Also, we present an analysis of the possible future
application of smart doors, a description of the ethical shortcomings, and
legislative issues.

提出了一篇关于智能门的位置论文，该论文首先指出了问题不仅涉及可靠性、气候控制、安全和操作模式，而且还需要通过对邻近门口人的社会背景和场景推理的复杂组合分析来深入理解社会背景，并对自动门进行了详尽的文献综述和未来应用的分析，以及对伦理和立法问题的描述。