Skeleton generation is essential for animating 3D assets, but current deep learning methods remain limited: they cannot handle the growing structural complexity of modern models and offer minimal controllability, creating a major bottleneck for real-world animation workflows. To address this, we propose an animator-centric SG framework that achieves high-quality skeleton prediction on complex inputs while providing intuitive control handles. Our contributions are threefold. First, we curate a large-scale dataset of 82,633 rigged meshes with diverse and complicated structures. Second, we introduce a novel semantic-aware tokenization scheme for auto-regressive modeling. This scheme effectively complements purely geometric prior methods by subdividing bones into semantically meaningful groups, thereby enhancing robustness to structural complexity and enabling a key control mechanism. Third, we design a learnable density interval module that allows animators to exert soft, direct control over bone density. Extensive experiments demonstrate that our framework not only generates high-quality skeletons for challenging inputs but also successfully fulfills two critical requirements from professional animators.

翻译：骨架生成对于3D资产的动画制作至关重要，但当前的深度学习方法仍存在局限性：它们无法处理现代模型日益复杂化的结构，且可控性极低，这给实际动画工作流程造成了重大瓶颈。为解决这一问题，我们提出了一种以动画师为中心的骨架生成框架，该框架能在复杂输入上实现高质量的骨架预测，同时提供直观的控制手段。我们的贡献包含三个方面。首先，我们构建了一个包含82,633个具有多样且复杂结构的绑定网格的大规模数据集。其次，我们引入了一种新颖的语义感知令牌化方案用于自回归建模。该方案通过将骨骼细分为语义上有意义的分组，有效补充了纯几何先验方法，从而增强了对结构复杂性的鲁棒性，并实现了一种关键控制机制。第三，我们设计了一个可学习的密度区间模块，使动画师能够对骨骼密度施加软性的、直接的控制。大量实验表明，我们的框架不仅能为具有挑战性的输入生成高质量骨架，还能成功满足专业动画师的两个关键需求。