论文标题
通过隐式重新投影网络的单眼人类数字化
Monocular Human Digitization via Implicit Re-projection Networks
论文作者
论文摘要
我们提出了一种从图像生成3D人类模型的方法。我们框架的关键是，我们可以从单个角度投影图像预测双面拼字深度图和颜色图像。我们的框架由三个网络组成。第一个网络预测正常地图可以恢复几何细节，例如衣服和面部区域的皱纹。第二个网络通过利用预测的正常地图来预测正面和背面视图的阴影被驱动的图像。最后一个多头网络同时拍摄正常地图和无阴影的图像，并预测深度图，同时通过多头注意门选择性地融合光度和几何信息。实验结果表明，我们的方法在视觉上出现的结果和竞争性能在各种评估指标上与最新方法有关。
We present an approach to generating 3D human models from images. The key to our framework is that we predict double-sided orthographic depth maps and color images from a single perspective projected image. Our framework consists of three networks. The first network predicts normal maps to recover geometric details such as wrinkles in the clothes and facial regions. The second network predicts shade-removed images for the front and back views by utilizing the predicted normal maps. The last multi-headed network takes both normal maps and shade-free images and predicts depth maps while selectively fusing photometric and geometric information through multi-headed attention gates. Experimental results demonstrate that our method shows visually plausible results and competitive performance in terms of various evaluation metrics over state-of-the-art methods.
