LAUSR

Answering the question “what is the most preferable view of a three-dimensional (3-D) human model?” is a challenge in computer vision, computer graphics, and cinematography applications because the appearance of a human, for a given pose, relies on the viewpoint of the user. Currently, to the best of the authors’ knowledge, solid research on the most preferable viewing angle for obtaining numerical subjective evaluation scores has not been conducted. In this study, we investigate a metric that can be used to quantify the view of a 3-D human model, whose value is maximized at the most favorable camera angle in accordance with subjective assessments done by users. For an objective assessment in a numerical form, in this study, we define three view selection metrics: the 1) normalized limb length sum; 2) normalized area of a two-dimensional bounding box; and 3) normalized visible area of a 3-D bounding box. Finally, we formulate a viewpoint optimization problem whose objective function is the sum of the metrics. However, the objective function is nonconcave, and the solution set of the constraint is nonconvex. To overcome this difficulty, we employ decomposition and penalty methods. From the simulation results, it is verified that the average of the viewpoint selection error between the ground truth viewpoint and the optimal viewpoint obtained by the proposed algorithm is very close to the lower bound of the viewpoint selection error.