Automatic Non-rigid 3D Modeling from Video

Lorenzo Torresani, Aaron Hertzmann

Abstract: We present a robust framework for estimating non-rigid 3D shape and motion in video sequences. Given an input video sequence, and a user-specified region to reconstruct, the algorithm automatically solves for the 3D time-varying shape and motion of the object, and estimates which pixels are outliers, while learning all system parameters, including a PDF over non-rigid deformations. There are no user-tuned parameters (other than initialization); all parameters are learned by maximizing the likelihood of the entire image stream. We apply our method to both rigid and non-rigid shape reconstruction, and demonstrate it in challenging cases of occlusion and variable illumination.