Self-learning Canonical Space for Multi-view 3D Human Pose Estimation

📄 arXiv: 2403.12440v2 📥 PDF

作者: Xiaoben Li, Mancheng Meng, Ziyan Wu, Terrence Chen, Fan Yang, Dinggang Shen

分类: cs.CV

发布日期: 2024-03-19 (更新: 2024-03-29)

💡 一句话要点

提出自学习规范空间以解决多视角3D人体姿态估计问题

🎯 匹配领域: 支柱六：视频提取与匹配 (Video Extraction)

关键词: 3D人体姿态估计 自监督学习 多视角信息 计算机视觉 虚拟现实 增强现实

📋 核心要点

  1. 现有的多视角3D人体姿态估计方法在准确标注相机姿态和3D姿态方面存在困难，影响了预测精度。
  2. 本文提出的CMANet框架通过自监督学习构建规范参数空间，整合视内和视间信息以提升3D姿态估计的准确性。
  3. 实验结果表明，CMANet在多个基准测试中表现优异，定量和定性分析均显示出显著的性能提升。

📝 摘要（中文）

多视角3D人体姿态估计相较于单视角方法具有优势，能够利用多视角图像提供的更全面信息。然而，准确标注这些信息非常困难，导致从多视角图像中预测准确的3D人体姿态面临挑战。为解决这一问题，本文提出了一种完全自监督的框架，称为级联多视角聚合网络（CMANet），以构建规范参数空间，全面整合和利用多视角信息。CMANet包括两个模块：视内模块（IRV）和视间模块（IEV），前者用于提取每个视角的初始相机姿态和3D人体姿态，后者则融合互补的姿态信息和跨视角3D几何信息，最终优化3D人体姿态。通过全面实验验证，CMANet在定量和定性分析中优于现有最先进方法。

🔬 方法详解

问题定义：本文旨在解决多视角3D人体姿态估计中的准确标注困难，现有方法在相机姿态和3D姿态的预测上存在不足。

核心思路：提出CMANet框架，通过自监督学习构建规范参数空间，整合视内和视间信息，从而提升3D姿态估计的准确性。

技术框架：CMANet由两个主要模块组成：视内模块（IRV）用于提取每个视角的初始相机姿态和3D人体姿态，视间模块（IEV）则用于融合互补的姿态信息和3D几何信息，最终优化3D姿态。整个过程分为两个阶段，第一阶段IRV学习相机姿态和视角依赖的3D姿态，第二阶段IEV进一步优化这些估计。

关键创新：CMANet的核心创新在于定义了一个规范参数空间，利用自监督学习有效整合多视角信息，显著提升了3D姿态估计的准确性。与现有方法相比，CMANet在信息整合和学习策略上具有本质区别。

关键设计：在设计中，IRV模块通过自监督学习利用2D关键点检测器的输出进行训练，IEV模块则通过联合拟合多视角2D关键点来优化3D姿态，确保了跨视角信息的有效利用。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，CMANet在多个基准测试中均优于现有最先进的方法，具体性能提升幅度达到XX%，在定量和定性分析中均表现出显著的优势，验证了其有效性和实用性。

🎯 应用场景

该研究在计算机视觉和人机交互等领域具有广泛的应用潜力，尤其是在虚拟现实、增强现实和运动分析等场景中。通过提高3D人体姿态估计的准确性，CMANet能够为这些应用提供更为精确的用户交互和行为分析能力，推动相关技术的发展。

📄 摘要（原文）

Multi-view 3D human pose estimation is naturally superior to single view one, benefiting from more comprehensive information provided by images of multiple views. The information includes camera poses, 2D/3D human poses, and 3D geometry. However, the accurate annotation of these information is hard to obtain, making it challenging to predict accurate 3D human pose from multi-view images. To deal with this issue, we propose a fully self-supervised framework, named cascaded multi-view aggregating network (CMANet), to construct a canonical parameter space to holistically integrate and exploit multi-view information. In our framework, the multi-view information is grouped into two categories: 1) intra-view information , 2) inter-view information. Accordingly, CMANet consists of two components: intra-view module (IRV) and inter-view module (IEV). IRV is used for extracting initial camera pose and 3D human pose of each view; IEV is to fuse complementary pose information and cross-view 3D geometry for a final 3D human pose. To facilitate the aggregation of the intra- and inter-view, we define a canonical parameter space, depicted by per-view camera pose and human pose and shape parameters ($θ$ and $β$) of SMPL model, and propose a two-stage learning procedure. At first stage, IRV learns to estimate camera pose and view-dependent 3D human pose supervised by confident output of an off-the-shelf 2D keypoint detector. At second stage, IRV is frozen and IEV further refines the camera pose and optimizes the 3D human pose by implicitly encoding the cross-view complement and 3D geometry constraint, achieved by jointly fitting predicted multi-view 2D keypoints. The proposed framework, modules, and learning strategy are demonstrated to be effective by comprehensive experiments and CMANet is superior to state-of-the-art methods in extensive quantitative and qualitative analysis.