论文标题
MASK2HAND：学习从阴影中预测3D手姿势和形状
Mask2Hand: Learning to Predict the 3D Hand Pose and Shape from Shadow
论文作者
论文摘要
我们提出了一种可自行的方法mask2hand，该方法学会了解决从2D手动剪影/阴影的2D二进制掩码预测3D手姿势和形状的具有挑战性的任务，而无需其他手动注重数据。鉴于相机空间中的固有摄像头参数和参数手模型，我们采用可区分的渲染技术将3D估计项目投影到2D二进制轮廓空间上。通过在渲染的轮廓和输入二进制面膜之间应用量身定制的损失组合，我们能够将自我援助机制集成到我们的端到端优化过程中，以限制全球网格注册和手部姿势估计。实验表明，我们的方法将单个二进制掩码作为输入，可以在不对齐和对齐设置作为需要RGB或深度输入的最新方法上实现可比的预测准确性。我们的代码可在https://github.com/lijenchang/mask2hand上找到。
We present a self-trainable method, Mask2Hand, which learns to solve the challenging task of predicting 3D hand pose and shape from a 2D binary mask of hand silhouette/shadow without additional manually-annotated data. Given the intrinsic camera parameters and the parametric hand model in the camera space, we adopt the differentiable rendering technique to project 3D estimations onto the 2D binary silhouette space. By applying a tailored combination of losses between the rendered silhouette and the input binary mask, we are able to integrate the self-guidance mechanism into our end-to-end optimization process for constraining global mesh registration and hand pose estimation. The experiments show that our method, which takes a single binary mask as the input, can achieve comparable prediction accuracy on both unaligned and aligned settings as state-of-the-art methods that require RGB or depth inputs. Our code is available at https://github.com/lijenchang/Mask2Hand.
