Capturing smooth motions from videos using markerless techniques typically
involves complex processes such as temporal constraints, multiple stages with
data-driven regression and optimization, and bundle solving over temporal
windows. These processes can be inefficient and require tuning multiple
objectives across stages. In contrast, BundleMoCap introduces a novel and
efficient approach to this problem. It solves the motion capture task in a
single stage, eliminating the need for temporal smoothness objectives while
still delivering smooth motions. BundleMoCap outperforms the state-of-the-art
without increasing complexity. The key concept behind BundleMoCap is manifold
interpolation between latent keyframes. By relying on a local manifold
smoothness assumption, we can efficiently solve a bundle of frames using a
single code. Additionally, the method can be 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 its
ability to achieve high-quality motion capture results with simplicity and
efficiency. More details can be found at this https URL

BundleMoCap 是一种基于无标记技术的高效、简洁的运动捕捉方法，通过利用局部流形平滑性假设和基于潜在关键帧的插值，能够在单个阶段中解决运动捕捉问题，提供平滑的运动效果，并且无需考虑时间平滑性目标。

BundleMoCap：稀疏多视角视频的高效、稳健和平滑动作捕捉

BundleMoCap: Efficient, Robust and Smooth Motion Capture from Sparse  Multiview Videos

Large-scale multiobjective optimization problems (LSMOPs) are characterized
as involving hundreds or even thousands of decision variables and multiple
conflicting objectives. An excellent algorithm for solving LSMOPs should find
Pareto-optimal solutions with diversity and escape from local optima in the
large-scale search space. Previous research has shown that these optimal
solutions are uniformly distributed on the manifold structure in the
low-dimensional space. However, traditional evolutionary algorithms for solving
LSMOPs have some deficiencies in dealing with this structural manifold,
resulting in poor diversity, local optima, and inefficient searches. In this
work, a generative adversarial network (GAN)-based manifold interpolation
framework is proposed to learn the manifold and generate high-quality solutions
on this manifold, thereby improving the performance of evolutionary algorithms.
We compare the proposed algorithm with several state-of-the-art algorithms on
large-scale multiobjective benchmark functions. Experimental results have
demonstrated the significant improvements achieved by this framework in solving
LSMOPs.

本文提出了一种基于生成对抗网络（GAN）的流形内插框架，以学习流形并在其上生成高质量解。我们将该算法与几种最先进的算法进行了比较，实验结果表明该框架在解决大规模多目标优化问题时取得了显著的改进。