Example-based Motion Synthesis via Generative Motion Matching

doi:10.1145/3592395

Example-based Motion Synthesis via Generative Motion Matching

2023-07-26

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AI

DOI：10.1145/3592395

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We present GenMM, a generative model that mines as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly.

Keywords:

motion synthesis

generative model

motion matching

Example-based Motion Synthesis via Generative Motion Matching

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