论文标题
OSSO：从外部获得骨骼形状
OSSO: Obtaining Skeletal Shape from Outside
论文作者
论文摘要
我们解决了从人体3D表面中推断一个人的解剖骨骼的问题；即，我们从外部（皮肤）预测内部（骨骼）。这在医学和生物力学上都有许多应用。现有的最先进的生物力学骨骼已详细介绍，但不容易推广到新主题。此外，预测骨骼的计算机视觉和图形方法通常是启发式方法，而不是从数据中学到的，不会利用完整的3D身体表面，并且未针对地面真理进行验证。据我们所知，我们的系统称为OSSO（从外部获得骨骼形状），是第一个从实际数据中学习从3D身体表面到内部骨骼的映射的系统。我们使用1000名男性和1000个女性双能X射线吸收法（DXA）扫描。对于这些，我们拟合一个参数3D体形模型（Star），以捕获身体表面和一种新型的基于零件的3D骨架模型以捕获骨骼。这提供了内部/外部训练对。我们使用PCA在姿势归一化空间中使用PCA对完整骨架的统计变化进行建模。然后，我们将回归器从体形参数训练到骨骼形状参数，并完善骨骼以满足对物理合理性的约束。给定任意的3D身体形状和姿势，Osso预测了内部逼真的骨骼。与以前的工作相反，我们在持有的DXA扫描中定量地评估了骨骼形状的准确性，从而优于最先进的骨架形状。我们还从各种和具有挑战性的3D机构中展示了3D骨架预测。可以从https://osso.is.tue.mpg.de/上进行研究来推断骨骼的代码，以及成对的外表面（皮肤）和骨骼（骨）网格的数据集，因为生物库返回数据集。这项研究是使用英国生物银行资源进行的。
We address the problem of inferring the anatomic skeleton of a person, in an arbitrary pose, from the 3D surface of the body; i.e. we predict the inside (bones) from the outside (skin). This has many applications in medicine and biomechanics. Existing state-of-the-art biomechanical skeletons are detailed but do not easily generalize to new subjects. Additionally, computer vision and graphics methods that predict skeletons are typically heuristic, not learned from data, do not leverage the full 3D body surface, and are not validated against ground truth. To our knowledge, our system, called OSSO (Obtaining Skeletal Shape from Outside), is the first to learn the mapping from the 3D body surface to the internal skeleton from real data. We do so using 1000 male and 1000 female dual-energy X-ray absorptiometry (DXA) scans. To these, we fit a parametric 3D body shape model (STAR) to capture the body surface and a novel part-based 3D skeleton model to capture the bones. This provides inside/outside training pairs. We model the statistical variation of full skeletons using PCA in a pose-normalized space. We then train a regressor from body shape parameters to skeleton shape parameters and refine the skeleton to satisfy constraints on physical plausibility. Given an arbitrary 3D body shape and pose, OSSO predicts a realistic skeleton inside. In contrast to previous work, we evaluate the accuracy of the skeleton shape quantitatively on held-out DXA scans, outperforming the state-of-the-art. We also show 3D skeleton prediction from varied and challenging 3D bodies. The code to infer a skeleton from a body shape is available for research at https://osso.is.tue.mpg.de/, and the dataset of paired outer surface (skin) and skeleton (bone) meshes is available as a Biobank Returned Dataset. This research has been conducted using the UK Biobank Resource.
