Searching Motion Graphs for Human Motion Synthesis

This work proposes a graph search based method for human motion sequence synthesis, complementing the modern generative model (e.g., variational auto-encoder or Gaussian process) based solutions that currently dominate this task and showing strong advantages at several aspects. The cornerstone of our method is a novel representation which we dub as motion graph. Each motion graph is scaffolded by a set of realistic human motion sequences (e.g., all training data in the Human3.6M benchmark). We devise a scheme that adds transition edges across different motion sequences, enabling more longer and diverse routes in the motion graph. Crucially, the proposed motion graph bridges the problem of human motion synthesis with graph-oriented combinatorial optimization, by naturally treating pre-specified starting or ending pose in human pose synthesis as end-points of the retrieved graph path. Based on a jump-sensitive graph path search algorithm proposed in this paper, our model can efficiently solve human motion completion over the motion graphs. In contrast, existing methods are mainly effective for human motion prediction and inadequate to impute missing sequences while jointly satisfying the two constraints of pre-specified starting / ending poses. For the case of only specifying the starting pose (i.e., human motion prediction), a forward graph walking from the starting node is first performed to sample a diverse set of ending nodes on the motion graph, each of which defines a motion completion problem. We conduct comprehensive experiments on two large-scale benchmarks (Human3.6M and HumanEva-I). The proposed method clearly proves to be superior in terms of several metrics, including the diversity of generated human motion sequences, affinity to real poses, and cross-scenario generalization etc.