论文标题
单眼实时全身捕获与部分间相关性
Monocular Real-time Full Body Capture with Inter-part Correlations
论文作者
论文摘要
我们提出了第一种实时全身捕获的方法，该方法与单个颜色图像中的动态3D面模型一起估算了身体和手的形状和运动。我们的方法使用了一种新的神经网络体系结构，该架构利用高计算效率的身体和手之间的相关性。与以前的作品不同，我们的方法是在小时，身体或面部的多个数据集上共同培训的，而无需同时注释所有零件的数据，这在足够的品种上很难创建。这种多数据训练的可能性可以提高卓越的概括能力。与较早的单眼全身方法相反，我们的方法通过估计统计面部模型的形状，表达，反照率和照明参数来捕获更具表现力的3D面部几何形状和颜色。我们的方法在公共基准上实现了竞争精度，同时更快地提供了更完整的面部重建。
We present the first method for real-time full body capture that estimates shape and motion of body and hands together with a dynamic 3D face model from a single color image. Our approach uses a new neural network architecture that exploits correlations between body and hands at high computational efficiency. Unlike previous works, our approach is jointly trained on multiple datasets focusing on hand, body or face separately, without requiring data where all the parts are annotated at the same time, which is much more difficult to create at sufficient variety. The possibility of such multi-dataset training enables superior generalization ability. In contrast to earlier monocular full body methods, our approach captures more expressive 3D face geometry and color by estimating the shape, expression, albedo and illumination parameters of a statistical face model. Our method achieves competitive accuracy on public benchmarks, while being significantly faster and providing more complete face reconstructions.
