基於範例的動作合成通過生成式動作匹配

摘要

我們提出了GenMM，一種生成模型，可以從單個或少量示例序列中「挖掘」盡可能多樣的動作。與現有的數據驅動方法形成鮮明對比，這些方法通常需要長時間的離線訓練，容易產生視覺異常，並且在大型和複雜骨架上容易失敗，GenMM繼承了無需訓練的特性，並具有優質的Motion Matching方法。GenMM可以在一秒內合成高質量的動作，即使是高度複雜和大型的骨架結構也能輕鬆應對。我們的生成框架的核心是生成式運動匹配模塊，它利用雙向視覺相似性作為生成成本函數來進行運動匹配，在多階段框架中逐步通過示例運動匹配來進行隨機猜測的改進。除了多樣的動作生成外，我們通過將其擴展到一些Motion Matching無法實現的場景，包括運動完成、關鍵幀引導生成、無限循環和運動重組，展示了我們生成框架的多功能性。本文的代碼和數據位於https://wyysf-98.github.io/GenMM/

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/

基於範例的動作合成通過生成式動作匹配

Example-based Motion Synthesis via Generative Motion Matching

摘要

Support