论文标题
两全其美的最好的：结合3D人体估计的基于模型和非参数的方法
The Best of Both Worlds: Combining Model-based and Nonparametric Approaches for 3D Human Body Estimation
论文作者
论文摘要
基于非参数的方法最近显示了从单眼图像重建人体的有希望的结果，而基于模型的方法可以帮助纠正这些估计并改善预测。但是，从全局图像特征中估算模型参数可能会导致估计的网格和图像证据之间明显的未对准。为了解决这个问题并利用两全其美的最好，我们提出了一个连续三个模块的框架。密集地图预测模块明确建立了图像证据与身体模型每个部分之间的密集紫外线对应关系。逆运动学模块完善了关键点预测并生成一个姿势的模板网格。最后，紫外线介绍模块依赖于相应的特征，预测和姿势模板，并完成了遮挡的身体形状的预测。我们的框架利用了基于非参数和基于模型的方法的最佳方法，并且对部分遮挡也很健壮。实验表明，我们的框架在多个公共基准上优于现有的3D人类估计方法。
Nonparametric based methods have recently shown promising results in reconstructing human bodies from monocular images while model-based methods can help correct these estimates and improve prediction. However, estimating model parameters from global image features may lead to noticeable misalignment between the estimated meshes and image evidence. To address this issue and leverage the best of both worlds, we propose a framework of three consecutive modules. A dense map prediction module explicitly establishes the dense UV correspondence between the image evidence and each part of the body model. The inverse kinematics module refines the key point prediction and generates a posed template mesh. Finally, a UV inpainting module relies on the corresponding feature, prediction and the posed template, and completes the predictions of occluded body shape. Our framework leverages the best of non-parametric and model-based methods and is also robust to partial occlusion. Experiments demonstrate that our framework outperforms existing 3D human estimation methods on multiple public benchmarks.
