text: With the increase number of companies focusing on commercializing Augmented Reality (AR), Virtual Reality (VR) and wearable devices, the need for a hand based input mechanism is becoming essential in order to make the experience natural, seamless and immersive. Hand pose estimation has progressed drastically in recent years due to the introduction of commodity depth cameras. Hand pose estimation based on vision is still a challenging problem due to its complexity from self-occlusion (between fingers), close similarity between fingers, dexterity of the hands, speed of the pose and the high dimension of the hand kinematic parameters. Articulated hand pose estimation is still an open problem and under intensive research from both academia and industry. The 2 approaches used for hand pose estimation are: discriminative and generative. Generative approach is a model based that tries to fit a hand model to the observed data. Discriminative approach is appearance based, usually implemented with machine learning (ML) and require a large amount of training data. Recent hand pose estimation uses hybrid approach by combining both discriminative and generative methods into a single hand pipeline. In this paper, we focus on reviewing recent progress of hand pose estimation from depth sensor. We will survey discriminative methods, generative methods and hybrid methods. This paper is not a comprehensive review of all hand pose estimation techniques, it is a subset of some of the recent state-of-the-art techniques.
  @cite_6: 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.
summary: A more recent paper @cite_6 in 2015 focused on comparing 13 hand pose estimation algorithms from a single depth frame and evaluated their performance on various publicly available dataset. Furthermore, @cite_6 created a new hand training dataset that is more diverse and complex to existing one. @cite_6 focus primarily on comparing the quality of each of the 13 algorithms using a common training dataset, this paper focus on reviewing the latest state-of-the-art hand pose estimation algorithms.