One major challenge for 3D pose estimation from a single RGB image is the acquisition of sufficient training data. In particular, collecting large amounts of training data that contain unconstrained images and are annotated with accurate 3D poses is infeasible. We therefore propose to use two independent training sources. The first source consists of images with annotated 2D poses and the second source consists of accurate 3D motion capture data. To integrate both sources, we propose a dual-source approach that combines 2D pose estimation with efficient and robust 3D pose retrieval. In our experiments, we show that our approach achieves state-of-the-art results when both sources are from the same dataset, but it also achieves competitive results when the motion capture data is taken from a different dataset.
The 3D pictorial structure model (PSM) proposed in @cite_30 combines generative and discriminative methods. Regression forests are trained to estimate the probabilities of 3D joint locations and the final 3D pose is inferred by the PSM. Since inference is performed in 3D, the bounding volume of the 3D pose space needs to be known and the inference requires a few minutes per frame.
@cite_30
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In this work we address the problem of estimating the 3D human pose from a single RGB image, which is a challenging problem since different 3D poses may have similar 2D projections. Following the success of regression forests for 3D pose estimation from depth data or 2D pose estimation from RGB images, we extend regression forests to infer missing depth data of image features and 3D pose simultaneously. Since we do not observe depth for inference or training directly, we hypothesize the depth of the features by sweeping with a plane through the 3D volume of potential joint locations. The regression forests are then combined with a pictorial structure framework, which is extended to 3D. The approach is evaluated on two challenging benchmarks where stateof-the-art performance is achieved.