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We propose a novel 3D neural network architecture for 3D hand pose estimationfrom a single depth image. Different from previous works that mostly run on 2Ddepth image domain and require intermediate or post process to bring in thesupervision from 3D space, we convert the depth map to a 3D volumetricrepresentation, and feed it into a 3D convolutional neural network(CNN) todirectly produce the pose in 3D requiring no further process. Our system doesnot require the ground truth reference point for initialization, and ournetwork architecture naturally integrates both local feature and global contextin 3D space. To increase the coverage of the hand pose space of the trainingdata, we render synthetic depth image by transferring hand pose from existingreal image datasets. We evaluation our algorithm on two public benchmarks andachieve the state-of-the-art performance. The synthetic hand pose dataset willbe available.

Hand3D: Hand Pose Estimation using 3D Neural Network