Example-based Motion Synthesis via Generative Motion Matching

• URL: http://arxiv.org/abs/2306.00378v1
• Date: Thu, 1 Jun 2023 06:19:33 GMT
• Title: Example-based Motion Synthesis via Generative Motion Matching
• Authors: Weiyu Li, Xuelin Chen, Peizhuo Li, Olga Sorkine-Hornung, Baoquan Chen
• Abstract summary: We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method.
• Score: 44.20519633463265
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Code and data for this paper are at https://wyysf-98.github.io/GenMM/

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