首次实现单次生成中可控融合多段动作，平滑自然且无需重训练。
Controllable Single-shot Animation Blending with Temporal Conditioning
- 通过时间条件控制生成过程，实现动作间无缝融合。
- 跨不同骨架与风格均生成流畅、可控的混合动作。
- 适合动画师快速迭代动作设计，提升创作效率。
在不依赖特定骨骼结构的情况下，仅用一段人类骨骼运动序列训练生成模型，已成为动画领域的重要研究方向。与文本到动作生成不同，单次生成模型允许动画师在不需额外数据或大量重训练的前提下，可控地生成现有动作模式的变体。然而，现有单次方法并未明确提供在单次生成中融合两个或多个动作的可控框架。本文提出首个单次动作融合框架，通过时间条件化生成过程实现平滑融合。该方法引入骨架感知归一化机制，指导动作间的过渡，从而实现对融合时机与方式的数据驱动式精准控制。我们在多种动画风格及不同骨骼结构上进行了广泛的定量与定性评估，结果表明，该方法能以统一高效的方式生成合理、平滑且可控的动作融合。
原文摘要 · Abstract (English)
Training a generative model on a single human skeletal motion sequence without being bound to a specific kinematic tree has drawn significant attention from the animation community. Unlike text-to-motion generation, single-shot models allow animators to controllably generate variations of existing motion patterns without requiring additional data or extensive retraining. However, existing single-shot methods do not explicitly offer a controllable framework for blending two or more motions within a single generative pass. In this paper, we present the first single-shot motion blending framework that enables seamless blending by temporally conditioning the generation process. Our method introduces a skeleton-aware normalization mechanism to guide the transition between motions, allowing smooth, data-driven control over when and how motions blend. We perform extensive quantitative and qualitative evaluations across various animation styles and different kinematic skeletons, demonstrating that our approach produces plausible, smooth, and controllable motion blends in a unified and efficient manner.
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