We propose RoHM, an approach for robust 3D human motion reconstruction from monocular RGB(-D) videos in the presence of noise and occlusions. Most previous approaches either train neural networks to directly regress motion in 3D or learn data-driven motion priors and combine them with optimization at test time. The former do not recover globally coherent motion and fail under occlusions; the latter are time-consuming, prone to local minima, and require manual tuning. To overcome these shortcomings, we exploit the iterative, denoising nature of diffusion models. RoHM is a novel diffusion-based motion model that, conditioned on noisy and occluded input data, reconstructs complete, plausible motions in consistent global coordinates. Given the complexity of the problem -- requiring one to address different tasks (denoising and infilling) in different solution spaces (local and global motion) -- we decompose it into two sub-tasks and learn two models, one for global trajectory and one for local motion. To capture the correlations between the two, we then introduce a novel conditioning module, combining it with an iterative inference scheme. We apply RoHM to a variety of tasks -- from motion reconstruction and denoising to spatial and temporal infilling. Extensive experiments on three popular datasets show that our method outperforms state-of-the-art approaches qualitatively and quantitatively, while being faster at test time. The code will be available at https://sanweiliti.github.io/ROHM/ROHM.html.

通过RoHM方法，在存在噪声和遮挡的单目RGB(-D)视频中，实现鲁棒的3D人体运动重建。将问题分解为全局轨迹和局部动作两个子任务，并引入了一个新的条件模块来捕捉两者之间的相关性，并结合迭代推理机制。在三个常用数据集上进行了大量实验，证明该方法在质量和数量上优于现有方法，并在测试时间上更快。

RoHM: 应用扩散方法实现稳健的人体动作重建