We present a light and efficient approach named Latent Fusion network for fast and accurate hand pose estimation from a single depth image. Our method innovatively decomposes 3D joint regression into 2D plane localization and 1D axis estimation from different spatial perspectives. We design multiple latent heatmap regression branches to predict hand pose separately and a fusion network to output the final result. Experiments on three public hand pose datasets (ICVL, NYU, MSRA) demonstrate that our system achieves state-of-the-art accuracy. Moreover, our method outperforms all top-ranked approaches by a large margin both in terms of inference speed (nearly a thousand frames per second) and model size (less than 10 MB).

中文翻译：

---

来自空间分解潜在热图的轻快手姿势估计

我们提出了一种称为潜在融合网络的轻型高效方法，用于从单个深度图像中快速准确地估计手部姿势。我们的方法从不同的空间角度创新地将 3D 联合回归分解为 2D 平面定位和 1D 轴估计。我们设计了多个潜在热图回归分支来分别预测手部姿势，并设计了一个融合网络来输出最终结果。在三个公共手姿势数据集（ICVL、NYU、MSRA）上的实验表明，我们的系统达到了最先进的准确性。此外，我们的方法在推理速度（每秒近一千帧）和模型大小（小于 10 MB）方面都大大优于所有排名靠前的方法。