论文标题
野外弱监督的网状跨跨跨斜手部重建
Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild
论文作者
论文摘要
我们引入了一个简单有效的网络体系结构，用于单眼3D手姿势估计，该姿势由图像编码器组成，然后是通过直接3D手网格重建损失训练的网格卷积解码器。我们通过在YouTube视频中收集大规模手动操作数据集来训练我们的网络，并将其用作弱监督的来源。我们基于网状卷积的弱监督系统的系统在很大程度上优于最先进的方法，甚至将野生基准中的错误减半。数据集和其他资源可在https://arielai.com/mesh_hands上找到。
We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss. We train our network by gathering a large-scale dataset of hand action in YouTube videos and use it as a source of weak supervision. Our weakly-supervised mesh convolutions-based system largely outperforms state-of-the-art methods, even halving the errors on the in the wild benchmark. The dataset and additional resources are available at https://arielai.com/mesh_hands.
