Numerous sign language datasets exist, yet they typically cover only a
limited selection of the thousands of signs used globally. Moreover, creating
diverse sign language datasets is an expensive and challenging task due to the
costs associated with gathering a varied group of signers. Motivated by these
challenges, we aimed to develop a solution that addresses these limitations. In
this context, we focused on textually describing body movements from skeleton
keypoint sequences, leading to the creation of a new dataset. We structured
this dataset around AUTSL, a comprehensive isolated Turkish sign language
dataset. We also developed a baseline model, SkelCap, which can generate
textual descriptions of body movements. This model processes the skeleton
keypoints data as a vector, applies a fully connected layer for embedding, and
utilizes a transformer neural network for sequence-to-sequence modeling. We
conducted extensive evaluations of our model, including signer-agnostic and
sign-agnostic assessments. The model achieved promising results, with a ROUGE-L
score of 0.98 and a BLEU-4 score of 0.94 in the signer-agnostic evaluation. The
dataset we have prepared, namely the AUTSL-SkelCap, will be made publicly
available soon.

我们开发了一个新的数据集 AUTSL-SkelCap，该数据集以土耳其独立手语 (AUTSL) 为基础，通过对骨骼关键点序列的文字描述来生成。我们还开发了一个基线模型 SkelCap，用于生成身体动作的文字描述。通过对模型进行广泛的评价，包括对签名者无关和手势无关的评估，我们获得了令人满意的结果，签名者无关评估中的 ROUGE-L 得分为 0.98，BLEU-4 得分为 0.94。我们准备好的数据集 AUTSL-SkelCap 将很快公开发布。

SkelCap: 从骨架关键点序列自动生成描述文本

SkelCap: Automated Generation of Descriptive Text from Skeleton Keypoint  Sequences

The analysis of the ubiquitous human-human interactions is pivotal for
understanding humans as social beings. Existing human-human interaction
datasets typically suffer from inaccurate body motions, lack of hand gestures
and fine-grained textual descriptions. To better perceive and generate
human-human interactions, we propose Inter-X, a currently largest human-human
interaction dataset with accurate body movements and diverse interaction
patterns, together with detailed hand gestures. The dataset includes ~11K
interaction sequences and more than 8.1M frames. We also equip Inter-X with
versatile annotations of more than 34K fine-grained human part-level textual
descriptions, semantic interaction categories, interaction order, and the
relationship and personality of the subjects. Based on the elaborate
annotations, we propose a unified benchmark composed of 4 categories of
downstream tasks from both the perceptual and generative directions. Extensive
experiments and comprehensive analysis show that Inter-X serves as a testbed
for promoting the development of versatile human-human interaction analysis.
Our dataset and benchmark will be publicly available for research purposes.

通过收集准确的身体动作和多样的交互模式以及详细的手势，提出了目前最大、最全面的人际交互数据集 Inter-X，旨在促进多方面的人际交互分析研究。

Inter-X: 人际互动分析的可变通性

Inter-X: Towards Versatile Human-Human Interaction Analysis

As a communication channel, body movements have been widely explored in
behavioral studies and kinesics. Performing and visual arts share the same
interests but focus on documenting and representing human body movements, such
as for dance notation and visual work creation. This paper investigates body
movements in oriental calligraphy and how to apply calligraphy principles to
stimulate and archive body movements. Through an artwork (Wushu), the authors
experiment with an interactive and generative approach to engage the audience's
bodily participation and archive the body movements as a compendium of
generated calligraphy. The audience assumes the role of both writers and
readers; creating ("writing") and appreciating ("reading") the generated
calligraphy becomes a cyclical process within this infinite "Book," which can
motivate further attention and discussions concerning Chinese characters and
calligraphy.

这篇论文研究了东方书法中的身体动作以及如何应用书法原则来激发和存档身体动作，通过艺术作品（武术），作者们尝试使用互动生成的方式来促使观众的身体参与，并将身体动作作为生成书法的集合进行存档。观众在这本无限的 “书” 中扮演着作家和读者的角色，创作（“写作”）和欣赏（“阅读”）生成的书法成为一个循环的过程，可以引起对汉字和书法的进一步关注和讨论。

身体动作存档：汉字书法的集体创作

Archiving Body Movements: Collective Generation of Chinese Calligraphy

This work presents computational methods for transferring body movements from
one person to another with videos collected in the wild. Specifically, we train
a personalized model on a single video from the Internet which can generate
videos of this target person driven by the motions of other people. Our model
is built on two generative networks: a human (foreground) synthesis net which
generates photo-realistic imagery of the target person in a novel pose, and a
fusion net which combines the generated foreground with the scene (background),
adding shadows or reflections as needed to enhance realism. We validate the the
efficacy of our proposed models over baselines with qualitative and
quantitative evaluations as well as a subjective test.

本文提出了一种利用计算方法在不同人之间传递身体动作的技术，基于生成网络，可以通过单个网络视频训练个人模型并生成具有照片级真实感的虚拟影像。