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This paper describes multiple model estimation for motion analysis and segmentation (aka spatial partitioning), from point correspondences in two successive images. In motion analysis applications, available (training) data is generated by several unknown models (motions). However, the correspondence between data samples and different models (motions) is unknown. Hence, the goal of learning (motion estimation) is two-fold, i.e. estimation (learning) of unknown motions (models) and separation (segmentation) of available data into several subsets corresponding to different motions. We present the mathematical formulation for multiple motion estimation, as a problem of learning several (regression) mappings, from a single data set, and then show a constructive (SVM-based) learning algorithm developed for this setting. Experimental results show potential advantages of the proposed method.