BundleMoCap++:从稀疏的多视角视频中高效、稳健、流畅地捕捉动作

IF 4.3 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computer Vision and Image Understanding Pub Date : 2024-10-04 DOI:10.1016/j.cviu.2024.104190
Georgios Albanis , Nikolaos Zioulis , Kostas Kolomvatsos
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引用次数: 0

摘要

无需专业设备和标记,就能从稀疏视频中生成平滑准确的运动图像,是研究界长期存在的问题。大多数方法通常涉及复杂的过程,如时间约束、结合数据驱动回归和优化技术的多阶段,以及在时间窗口上的捆绑求解。这些都增加了计算负担,并带来了针对不同目标项调整超参数的挑战。相比之下,BundleMoCap++ 为这一问题提供了一种简单而有效的方法。它只需一个阶段就能解决运动问题,无需时间平滑目标,同时还能在不影响精度的情况下实现平滑运动。在不增加复杂性的情况下,BundleMoCap++ 超越了最先进的技术。我们的方法基于潜在关键帧之间的流形插值。通过依赖局部流形平滑性假设和适当的插值方案,我们使用两个或更多潜在代码高效地解决了帧束问题。此外,该方法是以滑动窗口优化的方式实现的,只需要对第一帧进行适当的初始化,从而减轻了整体的计算负担。BundleMoCap++ 的优势在于用较少的计算资源获得高质量的运动捕捉结果。为了高效地实现这一目标,我们提出了一种新颖的人体姿态先验,该先验侧重于潜空间的几何方面,将其建模为超球,从而可以引入复杂的插值技术。我们还提出了一种直接在所学流形上优化潜变量的算法,从而提高了收敛性和性能。最后,我们引入了适用于超球的高阶插值技术,允许我们增加求解时间窗口,从而提高性能和效率。
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BundleMoCap++: Efficient, robust and smooth motion capture from sparse multiview videos
Producing smooth and accurate motions from sparse videos without requiring specialized equipment and markers is a long-standing problem in the research community. Most approaches typically involve complex processes such as temporal constraints, multiple stages combining data-driven regression and optimization techniques, and bundle solving over temporal windows. These increase the computational burden and introduce the challenge of hyperparameter tuning for the different objective terms. In contrast, BundleMoCap++ offers a simple yet effective approach to this problem. It solves the motion in a single stage, eliminating the need for temporal smoothness objectives while still delivering smooth motions without compromising accuracy. BundleMoCap++ outperforms the state-of-the-art without increasing complexity. Our approach is based on manifold interpolation between latent keyframes. By relying on a local manifold smoothness assumption and appropriate interpolation schemes, we efficiently solve a bundle of frames using two or more latent codes. Additionally, the method is implemented as a sliding window optimization and requires only the first frame to be properly initialized, reducing the overall computational burden. BundleMoCap++’s strength lies in achieving high-quality motion capture results with fewer computational resources. To do this efficiently, we propose a novel human pose prior that focuses on the geometric aspect of the latent space, modeling it as a hypersphere, allowing for the introduction of sophisticated interpolation techniques. We also propose an algorithm for optimizing the latent variables directly on the learned manifold, improving convergence and performance. Finally, we introduce high-order interpolation techniques adapted for the hypersphere, allowing us to increase the solving temporal window, enhancing performance and efficiency.
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来源期刊
Computer Vision and Image Understanding
Computer Vision and Image Understanding 工程技术-工程:电子与电气
CiteScore
7.80
自引率
4.40%
发文量
112
审稿时长
79 days
期刊介绍: The central focus of this journal is the computer analysis of pictorial information. Computer Vision and Image Understanding publishes papers covering all aspects of image analysis from the low-level, iconic processes of early vision to the high-level, symbolic processes of recognition and interpretation. A wide range of topics in the image understanding area is covered, including papers offering insights that differ from predominant views. Research Areas Include: • Theory • Early vision • Data structures and representations • Shape • Range • Motion • Matching and recognition • Architecture and languages • Vision systems
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