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/

GenMM是一种生成式模型，使得从一个或少量样例序列中挖掘尽可能多的多样化运动成为可能。它采用双向视觉相似性作为生成成本函数进而采取多阶段框架渐进式生成运动，这使得生成高质量运动的时间大大减少，同时实现了各种场景的应用，包括运动完成、关键帧指导生成、无限循环和运动重组。

基于示例的动作合成与生成式动作匹配技术