A novel joint points and silhouette-based method to estimate 3D human pose and shape
This work provides an incremental improvement for 3D human pose and shape estimation from sparse views, which could benefit applications requiring 3D human reconstruction from limited camera setups.
This paper proposes a method for 3D human pose and shape estimation from sparse-view images using a parametric model. The method fits the model to deep learning-estimated joint points and then minimizes a novel energy function based on 2D and 3D silhouette correspondence, demonstrating competitive performance on synthetic and real data.
This paper presents a novel method for 3D human pose and shape estimation from images with sparse views, using joint points and silhouettes, based on a parametric model. Firstly, the parametric model is fitted to the joint points estimated by deep learning-based human pose estimation. Then, we extract the correspondence between the parametric model of pose fitting and silhouettes on 2D and 3D space. A novel energy function based on the correspondence is built and minimized to fit parametric model to the silhouettes. Our approach uses sufficient shape information because the energy function of silhouettes is built from both 2D and 3D space. This also means that our method only needs images from sparse views, which balances data used and the required prior information. Results on synthetic data and real data demonstrate the competitive performance of our approach on pose and shape estimation of the human body.