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 https://moverseai.github.io/bundle/.

翻译：利用无标记技术从视频中捕捉平滑运动通常涉及复杂流程，例如时间约束、多阶段数据驱动回归与优化，以及时间窗口内的捆绑求解。这些流程效率低下，需要跨阶段调整多个优化目标。相比之下，BundleMoCap提出了一种新颖高效的方法：它通过单阶段解决运动捕捉任务，无需引入时间平滑性目标即可生成平滑运动。BundleMoCap在不增加复杂度的情况下超越了现有最佳方法。其核心思想在于潜在关键帧之间的流形插值：基于局部流形平滑性假设，我们可以利用单一编码高效求解帧束。此外，该方法可通过滑动窗口优化实现，仅需对首帧进行恰当初始化，从而降低整体计算负担。BundleMoCap的优势在于以简洁高效的方式实现高质量运动捕捉。更多详情请见https://moverseai.github.io/bundle/。