基于示例的动作合成通过生成式动作匹配

摘要

我们提出了GenMM，这是一个生成模型，可以从单个或少量示例序列中“挖掘”尽可能多样化的动作。与现有的数据驱动方法形成鲜明对比，后者通常需要长时间的离线训练，容易出现视觉伪影，并且往往无法处理大型和复杂的骨骼结构。GenMM继承了著名的运动匹配方法的无需训练特性和卓越的质量。GenMM可以在一秒钟内合成高质量的动作，即使是高度复杂和大型的骨骼结构也能胜任。我们的生成框架的核心是生成式运动匹配模块，它利用双向视觉相似性作为生成成本函数进行运动匹配，并在多阶段框架中通过示例运动匹配逐渐优化随机猜测。除了多样化的动作生成，我们通过将其扩展到一些仅通过运动匹配无法实现的场景，包括运动完成、关键帧引导生成、无限循环和运动重组，展示了我们生成框架的多功能性。本文的代码和数据可在https://wyysf-98.github.io/GenMM/找到。

English

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/

基于示例的动作合成通过生成式动作匹配

Example-based Motion Synthesis via Generative Motion Matching

摘要

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