An improved method for 3D shape estimation using active shape model

This paper tackles the problem of reconstructing 3D human poses from 2D landmarks, which is still an ill-posed problem. A widely-used approach is active shape model (ASM) which considers an unknown 3D shape as a linear combination of predefined basis shapes. The existing methods often resolve an optimization problem to reckon the weights and viewpoints of basis shapes, but they could fall into a locally-optimal and/or not use in the real-time system. In this paper, we propose an improved method by doing categorize database into subspaces to reduce execution time and make reconstruction accuracy better in four steps: (i) Separating 3D shapes in training database into subspaces based on their features. (ii) Learning predefined basis shapes of each subspace. (iii) Reconstructing 3D human poses from basis shapes of all subspaces. (iv) Picking out the best shape among them as the final result.