Modeling Time-Varying Illumination Patterns in Video
Recreating the temporal illumination variations of natural scenes has great potential for realistic synthesis of video sequences. In this paper, we present a 3D (model-based) approach that achieves this goal. The approach requires a training sequence to learn the time-varying illumination models, which can then be used for synthesis in another sequence. The motion and illumination parameters in the training sequence are estimated alternately by projecting onto appropriate basis functions of a bilinear space defined in terms of the 3D surface normals of the objects. The motion is represented in terms of 3D translation and rotation of the object centroid in the camera frame, and the illumination is represented using a spherical harmonics linear basis. We show video synthesis results using the proposed approach.