Human-centric Point Cloud Video Understanding (PVU) is an emerging field
focused on extracting and interpreting human-related features from sequences of
human point clouds, further advancing downstream human-centric tasks and
applications. Previous works usually focus on tackling one specific task and
rely on huge labeled data, which has poor generalization capability.
Considering that human has specific characteristics, including the structural
semantics of human body and the dynamics of human motions, we propose a unified
framework to make full use of the prior knowledge and explore the inherent
features in the data itself for generalized human-centric point cloud video
understanding. Extensive experiments demonstrate that our method achieves
state-of-the-art performance on various human-related tasks, including action
recognition and 3D pose estimation. All datasets and code will be released
soon.

提出了一种统一框架，通过充分利用先验知识和探索数据本身中的内在特征，实现了广义人为中心的点云视频理解，包括动作识别和 3D 姿态估计等任务，并在各种人相关任务中达到了最先进的性能。

人体为中心点云视频理解的统一框架

A Unified Framework for Human-centric Point Cloud Video Understanding

3D landmark detection plays a pivotal role in various applications such as 3D
registration, pose estimation, and virtual try-on. While considerable success
has been achieved in 2D human landmark detection or pose estimation, there is a
notable scarcity of reported works on landmark detection in unordered 3D point
clouds. This paper introduces a novel challenge, namely 3D landmark detection
on human point clouds, presenting two primary contributions. Firstly, we
establish a comprehensive human point cloud dataset, named HPoint103, designed
to support the 3D landmark detection community. This dataset comprises 103
human point clouds created with commercial software and actors, each manually
annotated with 11 stable landmarks. Secondly, we propose a Dual Cascade Point
Transformer (D-CPT) model for precise point-based landmark detection. D-CPT
gradually refines the landmarks through cascade Transformer decoder layers
across the entire point cloud stream, simultaneously enhancing landmark
coordinates with a RefineNet over local regions. Comparative evaluations with
popular point-based methods on HPoint103 and the public dataset DHP19
demonstrate the dramatic outperformance of our D-CPT. Additionally, the
integration of our RefineNet into existing methods consistently improves
performance.

本篇论文介绍了一种在人体点云上进行三维关键点检测的挑战，并提出了一种名为 Dual Cascade Point Transformer (D-CPT) 的模型，该模型通过级联的 Transformer 解码器逐步优化关键点，在本地区域使用 RefineNet 增强关键点坐标，通过在 HPoint103 和公共数据集 DHP19 上进行对比评估，验证了我们的 D-CPT 模型在关键点检测方面的显著性能优势，并表明将我们的 RefineNet 集成到现有方法中可以持续提高性能。

基于人体点云的三维特征点检测：一个基准和双级级联点变换器框架

3D Landmark Detection on Human Point Clouds: A Benchmark and A Dual  Cascade Point Transformer Framework

The emergence of digital avatars has raised an exponential increase in the
demand for human point clouds with realistic and intricate details. The
compression of such data becomes challenging with overwhelming data amounts
comprising millions of points. Herein, we leverage the human geometric prior in
geometry redundancy removal of point clouds, greatly promoting the compression
performance. More specifically, the prior provides topological constraints as
geometry initialization, allowing adaptive adjustments with a compact parameter
set that could be represented with only a few bits. Therefore, we can envisage
high-resolution human point clouds as a combination of geometric priors and
structural deviations. The priors could first be derived with an aligned point
cloud, and subsequently the difference of features is compressed into a compact
latent code. The proposed framework can operate in a play-and-plug fashion with
existing learning based point cloud compression methods. Extensive experimental
results show that our approach significantly improves the compression
performance without deteriorating the quality, demonstrating its promise in a
variety of applications.

本文提出了一种基于人体几何先验知识的点云压缩框架，通过减少几何冗余以及提供拓扑学约束，实现了对高分辨率的人体点云数据的压缩，该方法在不降低数据质量的情况下，显著提高了压缩性能。