通过高效的二维线索从单目 RGB 进行三维手部姿势和形状估计

IF 17.3 3区 计算机科学 Q1 COMPUTER SCIENCE, SOFTWARE ENGINEERING Computational Visual Media Pub Date : 2023-11-30 DOI:10.1007/s41095-023-0346-4
Fenghao Zhang, Lin Zhao, Shengling Li, Wanjuan Su, Liman Liu, Wenbing Tao
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引用次数: 0

摘要

根据单视角 RGB 图像估计三维手形对许多应用都很重要。然而,手部形状和姿势的多样性、深度模糊性和遮挡可能会导致姿势错误和手部网格噪声。充分利用二维姿势等二维线索可以有效提高三维人体手形估计的质量。在本文中,我们利用二维联合热图来获取空间细节,从而实现稳健的姿势估计。我们还引入了与深度无关的二维网格,以避免网格回归中的深度模糊,从而实现高效的手部图像配准。我们的方法有四个级联阶段:二维线索提取、姿势特征编码、初始重建和重建完善。具体来说,我们首先对图像进行编码,以便在二维线索提取过程中确定语义特征;这也用于预测手部关节和进行分割。然后,在姿势特征编码阶段,我们使用手部关节编码器从关节热图中学习空间信息。接着,在初始重建步骤中获得粗略的三维手部网格和二维网格;网格挤压和激发块用于融合不同的手部特征,以增强对三维手部结构的感知。最后,全局网格细化阶段从预测的 2D 网格中学习手部网格顶点之间的非局部关系,从而预测偏移手部网格,对重建结果进行微调。FreiHAND 基准数据集的定量和定性结果表明,我们的方法达到了最先进的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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3D hand pose and shape estimation from monocular RGB via efficient 2D cues

Estimating 3D hand shape from a single-view RGB image is important for many applications. However, the diversity of hand shapes and postures, depth ambiguity, and occlusion may result in pose errors and noisy hand meshes. Making full use of 2D cues such as 2D pose can effectively improve the quality of 3D human hand shape estimation. In this paper, we use 2D joint heatmaps to obtain spatial details for robust pose estimation. We also introduce a depth-independent 2D mesh to avoid depth ambiguity in mesh regression for efficient hand-image alignment. Our method has four cascaded stages: 2D cue extraction, pose feature encoding, initial reconstruction, and reconstruction refinement. Specifically, we first encode the image to determine semantic features during 2D cue extraction; this is also used to predict hand joints and for segmentation. Then, during the pose feature encoding stage, we use a hand joints encoder to learn spatial information from the joint heatmaps. Next, a coarse 3D hand mesh and 2D mesh are obtained in the initial reconstruction step; a mesh squeeze-and-excitation block is used to fuse different hand features to enhance perception of 3D hand structures. Finally, a global mesh refinement stage learns non-local relations between vertices of the hand mesh from the predicted 2D mesh, to predict an offset hand mesh to fine-tune the reconstruction results. Quantitative and qualitative results on the FreiHAND benchmark dataset demonstrate that our approach achieves state-of-the-art performance.

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来源期刊
Computational Visual Media
Computational Visual Media Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
16.90
自引率
5.80%
发文量
243
审稿时长
6 weeks
期刊介绍: Computational Visual Media is a peer-reviewed open access journal. It publishes original high-quality research papers and significant review articles on novel ideas, methods, and systems relevant to visual media. Computational Visual Media publishes articles that focus on, but are not limited to, the following areas: • Editing and composition of visual media • Geometric computing for images and video • Geometry modeling and processing • Machine learning for visual media • Physically based animation • Realistic rendering • Recognition and understanding of visual media • Visual computing for robotics • Visualization and visual analytics Other interdisciplinary research into visual media that combines aspects of computer graphics, computer vision, image and video processing, geometric computing, and machine learning is also within the journal''s scope. This is an open access journal, published quarterly by Tsinghua University Press and Springer. The open access fees (article-processing charges) are fully sponsored by Tsinghua University, China. Authors can publish in the journal without any additional charges.
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