论文标题
野外3D人姿势估计的概括
Towards Generalization of 3D Human Pose Estimation In The Wild
论文作者
论文摘要
在本文中，我们提出了3DBodyTex.pose，该数据集解决了3D人类姿势估计的任务。由于缺乏足够的数据集，对野外图像的概括仍然有限。现有的通常收集在室内受控环境中，其中使用运动捕获系统来获得人类的3D地面真相注释。 3DBodyTex.Pose提供了高质量和丰富的数据，其中包含405位不同的衣服和姿势中的不同主题，以及带有地面2D和3D姿势注释的81K图像样品。这些图像是从200个观点中产生的，其中有70个具有挑战性的极端观点。这些数据是从高分辨率纹理的3D车身扫描开始创建的，并结合了各种现实的背景。使用3DBodytex增强的数据来重新训练最先进的3D姿势估计方法。pose在整体绩效方面表现出了有希望的改善，并且在对挑战性观点进行测试时，每个关节位置误差的明智下降。预计3DBodyTex.pose将为研究社区提供新的可能性，以概括从单眼内部图像中估算3D姿势。
In this paper, we propose 3DBodyTex.Pose, a dataset that addresses the task of 3D human pose estimation in-the-wild. Generalization to in-the-wild images remains limited due to the lack of adequate datasets. Existent ones are usually collected in indoor controlled environments where motion capture systems are used to obtain the 3D ground-truth annotations of humans. 3DBodyTex.Pose offers high quality and rich data containing 405 different real subjects in various clothing and poses, and 81k image samples with ground-truth 2D and 3D pose annotations. These images are generated from 200 viewpoints among which 70 challenging extreme viewpoints. This data was created starting from high resolution textured 3D body scans and by incorporating various realistic backgrounds. Retraining a state-of-the-art 3D pose estimation approach using data augmented with 3DBodyTex.Pose showed promising improvement in the overall performance, and a sensible decrease in the per joint position error when testing on challenging viewpoints. The 3DBodyTex.Pose is expected to offer the research community with new possibilities for generalizing 3D pose estimation from monocular in-the-wild images.
