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.

翻译：以非参数为基础的方法最近显示,在用单视图像重建人体方面,以模型为基础的方法可以帮助纠正这些估计并改进预测。然而,根据全球图像特征估计模型参数可能会导致估计的 meshes 和图像证据之间明显不匹配。为了解决这一问题并利用两个世界的最佳手段,我们提议了一个连续三个模块的框架。一个密集的地图预测模块明确确定了图像证据与身体模型每个部分之间的密集紫外线对应。反动学模块改进了关键点预测并生成了一个成型模板网格。最后,一个紫外线插入模块依赖于相应的特征、预测和成型模板,并完成对隐蔽体形状的预测。我们的框架利用了非参数和模型法的最佳方法,并且也能够对部分封闭进行强力。实验表明,我们的框架比现有的3D人类估算方法更符合多个公共基准。