Hand pose estimation has matured rapidly in recent years. The introduction of commodity depth sensors and a multitude of practical applications have spurred new advances. We provide an extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame. To do so, we have implemented a considerable number of systems, and have released software and evaluation code. We summarize important conclusions here: (1) Coarse pose estimation appears viable for scenes with isolated hands. However, high precision pose estimation [required for immersive virtual reality and cluttered scenes (where hands may be interacting with nearby objects and surfaces) remain a challenge. To spur further progress we introduce a challenging new dataset with diverse, cluttered scenes. (2) Many methods evaluate themselves with disparate criteria, making comparisons difficult. We define a consistent evaluation criteria, rigorously motivated by human experiments. (3) We introduce a simple nearest-neighbor baseline that outperforms most existing systems. This implies that most systems do not generalize beyond their training sets. This also reinforces the under-appreciated point that training data is as important as the model itself. We conclude with directions for future progress.

中文翻译：

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基于深度的手部姿势估计：方法、数据和挑战

近年来，手部姿态估计迅速成熟。商品深度传感器的引入和大量实际应用推动了新的进步。我们对最先进的技术进行了广泛的分析，重点是从单个深度帧估计手部姿势。为此，我们实施了相当多的系统，并发布了软件和评估代码。我们在这里总结了重要的结论：（1）粗姿态估计对于双手孤立的场景似乎是可行的。然而，高精度姿势估计 [沉浸式虚拟现实和杂乱场景（手可能与附近的物体和表面交互）所需的] 仍然是一个挑战。为了刺激进一步的进展，我们引入了具有多样化、杂乱场景的具有挑战性的新数据集。(2) 许多方法用不同的标准评估自己，使比较变得困难。我们定义了一个一致的评估标准，严格由人体实验驱动。(3) 我们引入了一个简单的最近邻基线，其性能优于大多数现有系统。这意味着大多数系统不会在其训练集之外进行泛化。这也强化了一个被低估的观点，即训练数据与模型本身一样重要。我们总结了未来进展的方向。