6. The method of claim 3 , wherein said creating the sequence of synthesized poses further comprises: identifying a collection of nearest neighbor feature vectors for each of the plurality of samples of the query stroke, wherein the collection of nearest neighbor feature vectors for each sample of the query stroke comprises two or more feature vectors for respective samples of one or more reference strokes that are most similar to the feature vector constructed for the sample of the query stroke; and wherein said identifying a best neighbor feature vector for each of the plurality of samples of the query stroke comprises selecting one of the nearest neighbor feature vectors from each of the collections of nearest neighbor feature vectors.
6. The method of claim 3 , wherein said creating the sequence of synthesized poses further comprises: identifying a collection of nearest neighbor feature vectors for each of the plurality of samples of the query stroke, wherein the collection of nearest neighbor feature vectors for each sample of the query stroke comprises two or more feature vectors for respective samples of one or more reference strokes that are most similar to the feature vector constructed for the sample of the query stroke; and wherein said identifying a best neighbor feature vector for each of the plurality of samples of the query stroke comprises selecting one of the nearest neighbor feature vectors from each of the collections of nearest neighbor feature vectors. 7. The method of claim 6 , wherein said selecting one of the nearest neighbor feature vectors from each of the collections of nearest neighbor feature vectors comprises applying dynamic programming to the collections of nearest neighbor feature vectors to determine the lowest cost sequence of nearest neighbor feature vectors.