UniMoGen可生成任意骨骼角色的自然动作，无需预设关节数。
UniMoGen: Universal Motion Generation
- 基于UNet的扩散模型，动态处理每具角色所需关节数。
- 在100style数据集上优于当前最优方法，跨骨骼训练效率更高。
- 支持风格、轨迹控制及动作延续，适合动画与游戏开发。
动作生成是计算机图形学、动画、游戏和机器人领域的核心任务，能生成真实多样的角色动作。现有方法受限于特定骨骼结构，难以跨角色通用。为此，我们提出UniMoGen——一种基于UNet的扩散模型，实现骨骼无关的动作生成。该模型可在包含人类和动物等多种角色的动作数据上训练，无需预设最大关节数。通过仅动态处理每个角色所需的关节点，模型同时实现骨骼无关性与计算高效性。UniMoGen支持通过风格和轨迹输入进行可控生成，并可从历史帧延续动作。在100style数据集上，其性能超越当前最优方法。当联合训练100style与LAFAN1数据集（使用不同骨骼）时，模型在两类骨骼上均表现出高精度与更优效率。结果表明，UniMoGen为多种角色动画提供了一种灵活、高效且可控的解决方案。
原文摘要 · Abstract (English)
Motion generation is a cornerstone of computer graphics, animation, gaming, and robotics, enabling the creation of realistic and varied character movements. A significant limitation of existing methods is their reliance on specific skeletal structures, which restricts their versatility across different characters. To overcome this, we introduce UniMoGen, a novel UNet-based diffusion model designed for skeleton-agnostic motion generation. UniMoGen can be trained on motion data from diverse characters, such as humans and animals, without the need for a predefined maximum number of joints. By dynamically processing only the necessary joints for each character, our model achieves both skeleton agnosticism and computational efficiency. Key features of UniMoGen include controllability via style and trajectory inputs, and the ability to continue motions from past frames. We demonstrate UniMoGen's effectiveness on the 100style dataset, where it outperforms state-of-the-art methods in diverse character motion generation. Furthermore, when trained on both the 100style and LAFAN1 datasets, which use different skeletons, UniMoGen achieves high performance and improved efficiency across both skeletons. These results highlight UniMoGen's potential to advance motion generation by providing a flexible, efficient, and controllable solution for a wide range of character animations.
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