论文标题
稳定性驱动的触点从单眼颜色图像重建
Stability-driven Contact Reconstruction From Monocular Color Images
论文作者
论文摘要
物理接触为手动状态重建提供了其他限制，以及进一步理解交互提供的基础。从单眼图像中估算这些严重遮挡的区域提出了一个巨大的挑战。现有方法优化由距离阈值或通过触点标记的数据集驱动的手动对象触点。但是，由于这些室内数据集中涉及的受试者和对象的数量受到限制，因此学习的接触模式无法轻易推广。我们的关键思想是直接从单眼图像中重建接触模式，然后利用模拟中的物理稳定性标准来优化它。该标准是由由物理引擎计算出的由物理引擎计算出的与现有解决方案计算的，我们的框架可以适应更个性化的手和多样化的对象形状的定义。此外，创建具有额外物理属性的交互数据集来验证我们方法的SIM到真实一致性。通过全面的评估，可以通过拟议的框架以准确性和稳定性来重建手动对象触点。
Physical contact provides additional constraints for hand-object state reconstruction as well as a basis for further understanding of interaction affordances. Estimating these severely occluded regions from monocular images presents a considerable challenge. Existing methods optimize the hand-object contact driven by distance threshold or prior from contact-labeled datasets. However, due to the number of subjects and objects involved in these indoor datasets being limited, the learned contact patterns could not be generalized easily. Our key idea is to reconstruct the contact pattern directly from monocular images, and then utilize the physical stability criterion in the simulation to optimize it. This criterion is defined by the resultant forces and contact distribution computed by the physics engine.Compared to existing solutions, our framework can be adapted to more personalized hands and diverse object shapes. Furthermore, an interaction dataset with extra physical attributes is created to verify the sim-to-real consistency of our methods. Through comprehensive evaluations, hand-object contact can be reconstructed with both accuracy and stability by the proposed framework.
