A comprehensive understanding of interested human-to-human interactions in
video streams, such as queuing, handshaking, fighting and chasing, is of
immense importance to the surveillance of public security in regions like
campuses, squares and parks. Different from conventional human interaction
recognition, which uses choreographed videos as inputs, neglects concurrent
interactive groups, and performs detection and recognition in separate stages,
we introduce a new task named human-to-human interaction detection (HID). HID
devotes to detecting subjects, recognizing person-wise actions, and grouping
people according to their interactive relations, in one model. First, based on
the popular AVA dataset created for action detection, we establish a new HID
benchmark, termed AVA-Interaction (AVA-I), by adding annotations on interactive
relations in a frame-by-frame manner. AVA-I consists of 85,254 frames and
86,338 interactive groups, and each image includes up to 4 concurrent
interactive groups. Second, we present a novel baseline approach SaMFormer for
HID, containing a visual feature extractor, a split stage which leverages a
Transformer-based model to decode action instances and interactive groups, and
a merging stage which reconstructs the relationship between instances and
groups. All SaMFormer components are jointly trained in an end-to-end manner.
Extensive experiments on AVA-I validate the superiority of SaMFormer over
representative methods. The dataset and code will be made public to encourage
more follow-up studies.

该论文介绍了一种名为 HID 的新任务，旨在通过在同一模型中检测主体、识别人 - 人互动，并根据他们的互动关系对人进行分组，以此全面了解视频流中感兴趣的人 - 人互动，还在 AVA 数据集基础上建立了一个新的 HID 基准，称为 AVA-Interaction (AVA-I)，并描述了一个新的基线方法 SaMFormer，该方法表现出在 AVA-I 上的优越性。

人际交互检测

Human-to-Human Interaction Detection

We have recently seen tremendous progress in diffusion advances for
generating realistic human motions. Yet, they largely disregard the rich
multi-human interactions. In this paper, we present InterGen, an effective
diffusion-based approach that incorporates human-to-human interactions into the
motion diffusion process, which enables layman users to customize high-quality
two-person interaction motions, with only text guidance. We first contribute a
multimodal dataset, named InterHuman. It consists of about 107M frames for
diverse two-person interactions, with accurate skeletal motions and 16,756
natural language descriptions. For the algorithm side, we carefully tailor the
motion diffusion model to our two-person interaction setting. To handle the
symmetry of human identities during interactions, we propose two cooperative
transformer-based denoisers that explicitly share weights, with a mutual
attention mechanism to further connect the two denoising processes. Then, we
propose a novel representation for motion input in our interaction diffusion
model, which explicitly formulates the global relations between the two
performers in the world frame. We further introduce two novel regularization
terms to encode spatial relations, equipped with a corresponding damping scheme
during the training of our interaction diffusion model. Extensive experiments
validate the effectiveness and generalizability of InterGen. Notably, it can
generate more diverse and compelling two-person motions than previous methods
and enables various downstream applications for human interactions.

本文提出了一种有效的扩散性方法 InterGen，通过考虑人与人之间的交互并将其融入到运动扩散过程中，使普通用户只需使用文本指导即可自定义高质量的两人互动动作。