通过对齐高阶时序动态实现自然人体运动恢复
Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos
把逐关节的 2D 位置、3D 速度与 3D 加速度作为软约束，接入全局轨迹优化，可作为即插即用后处理模块，用于任何单目 HMR 管线，以抑制抖动与过度平滑；其 PVA-Net 的高阶时序预测思路也可迁移到其他需要速度/加速度先验的时序回归任务。
原文依据 PVA-Net, a temporal transformer that infers per-joint 2D positions, 3D velocities, and 3D accelerations directly from a monocular video
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论从单目视频中恢复的人体运动往往显得过于平滑或动态不一致，即使关节位置在数值上是准确的。我们观察到，这一局限源于缺乏可靠的高阶时序线索——速度与加速度——而它们对于重建具有真实动量、时序节奏和高频细节的运动至关重要。我们提出 HTD-Refine，一个后处理框架，利用显式估计的高阶时序动态来增强现有的人体运动恢复（HMR）管线。系统的核心是 PVA-Net，一个时序 Transformer，可直接从单目视频中推断逐关节的 2D 位置、3D 速度与 3D 加速度。这些预测的动态在全局优化过程中作为软性但信息丰富的约束，用于细化世界空间轨迹，显著减少抖动、抑制过度平滑，并恢复物理上合理的运动。在具有挑战性的野外基准上的大量实验表明，HTD-Refine 能持续改进最优的 HMR 方法，产生更准确的全局轨迹和明显更自然的运动动态。我们的结果凸显了高阶时序建模在推进单目人体运动恢复中的关键作用。
英文原文摘要 1309 字符
Human motion recovered from monocular videos often appears overly smooth or dynamically inconsistent, even when joint positions are numerically accurate. We observe that this limitation stems from the absence of reliable high-order temporal cues--velocity and acceleration--which are essential for reconstructing motion that exhibits realistic momentum, timing, and high-frequency detail. We introduce HTD-Refine, a post-processing framework that augments existing Human Motion Recovery (HMR) pipelines using explicitly estimated high-order temporal dynamics. At the core of our system is PVA-Net, a temporal transformer that infers per-joint 2D positions, 3D velocities, and 3D accelerations directly from a monocular video. These predicted dynamics serve as soft yet informative constraints in a global optimization procedure that refines world-space trajectories, significantly reducing jitter, suppressing oversmoothing, and restoring physically plausible motion. Extensive experiments on challenging in-the-wild benchmarks show that HTD-Refine consistently improves state-of-the-art HMR methods, yielding more accurate global trajectories and substantially more natural motion dynamics. Our results highlight the critical role of high-order temporal modeling in advancing monocular human motion recovery.