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/

翻译：我们提出GenMM，一种能够从单个或少量示例序列中“挖掘”出尽可能多样化运动的生成模型。与现有数据驱动方法形成鲜明对比的是，后者通常需要长时间离线训练、易出现视觉伪影、且在处理大型复杂骨骼结构时往往失败，而GenMM继承了著名的运动匹配方法无需训练、输出质量卓越的特性。GenMM能够在不到一秒的时间内合成高质量运动，即使面对高度复杂的大型骨骼结构也是如此。我们生成框架的核心是生成式运动匹配模块，该模块利用双向视觉相似性作为运动匹配的生成成本函数，并以多阶段框架运行，通过示例运动匹配逐步优化随机初始猜测。除了多样化的运动生成之外，我们还展示了生成框架的多功能性，将其扩展至单独使用运动匹配无法实现的一系列场景，包括运动补全、关键帧引导生成、无限循环以及运动重组。本文代码与数据见https://wyysf-98.github.io/GenMM/