It is challenging to directly estimate the human geometry from a single image due to the high diversity and complexity of body shapes with the various clothing styles. Most of model-based approaches are limited to predict the shape and pose of a minimally clothed body with over-smoothing surface. While capturing the fine detailed geometries, the model-free methods are lack of the fixed mesh topology. To address these issues, we propose a novel topology-preserved human reconstruction approach by bridging the gap between model-based and model-free human reconstruction. We present an end-to-end neural network that simultaneously predicts the pixel-aligned implicit surface and an explicit mesh model built by graph convolutional neural network. Experiments on DeepHuman and our collected dataset showed that our approach is effective. The code will be made publicly available.
翻译:直接从单一图像中估计人类几何是具有挑战性的,因为各种衣着风格的人体形状多种多样和复杂。基于模型的方法大多限于预测一个布满极少的、透透透表面的物体的形状和形状。在捕捉精细细细细的几何外形的同时,没有模型的方法缺乏固定网状地形。为了解决这些问题,我们建议采用新的、具有地形特征的人类重建方法,缩小基于模型的和没有模型的人类重建之间的差距。我们提出了一个端到端神经网络,同时预测与像素接轨的暗表和由图形共生神经网络建造的清晰的网状模型。关于深海人类的实验和我们收集的数据显示,我们的方法是有效的。代码将公布于众。