  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 will release all software and
evaluation code. We summarize important conclusions here: (1) Pose estimation
appears roughly solved for scenes with isolated hands. However, methods still
struggle to analyze cluttered scenes where hands may be interacting with nearby
objects and surfaces. 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.
