PoseMaster：面向风格化姿态生成的统一三维原生框架
PoseMaster: A Unified 3D Native Framework for Stylized Pose Generation
用 3D 骨架直接作为条件、在统一框架内端到端生成带风格的三维网格，替代「2D 姿态编辑 + 3D 提升」的级联流程；其严格空间对齐的骨架-网格配对可直接接自动蒙皮，为角色绑定与可动画资产生成提供可复用范式。
原文依据 we directly utilize the 3D skeleton because it can provide a more accurate representation of 3D spatial and topological relationships
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论姿态风格化旨在合成与目标姿态对齐的风格化内容，是 2D、3D 与视频领域的一项基础任务。在三维领域，现有方法通常依赖级联流程：先借助 2D 基础模型对图像姿态进行编辑，再将其提升为三维表示。然而这一范式限制了三维姿态风格化的精度与多样性。为此，我们提出一种新的三维姿态风格化范式，将姿态风格化与三维生成统一在一个连贯框架之内。这种整合降低了累积误差的风险，并提升了模型的效率与效果。此外，与以往通常使用 2D 骨架图像作为引导的工作不同，我们直接使用 3D 骨架，因为它能够更准确地表达三维空间与拓扑关系，从而显著增强模型实现更丰富、更精确的姿态风格化的能力。我们还开发了一个可扩展的数据引擎，用于构建大规模的「图像-骨架-网格」三元组数据集，使模型能够联合学习身份保持与几何对齐。大量实验表明，PoseMaster 在定性指标与定量指标上均显著优于最优方法。得益于生成的三维网格与条件骨架之间严格的空间对齐，PoseMaster 与自动化蒙皮模型结合时可直接创建可动画资产，凸显了其在自动化角色绑定方面的显著潜力。
英文原文摘要 1637 字符
Pose stylization, which aims to synthesize stylized content aligning with target poses, serves as a fundamental task across 2D, 3D, and video domains. In the 3D realm, prevailing approaches typically rely on a cascade pipeline: first manipulating the image pose via 2D foundation models and subsequently lifting it into 3D representations. However, this paradigm limits the precision and diversity of the 3d pose stylization. To this end, we propose a novel paradigm for 3D pose stylization that unifies pose stylization and 3D generation within a cohesive framework. This integration minimizes the risk of cumulative errors and enhances the model's efficiency and effectiveness. In addition, diverging from previous works that typically utilize 2D skeleton images as guidance, we directly utilize the 3D skeleton because it can provide a more accurate representation of 3D spatial and topological relationships, which significantly enhances the model's capacity to achieve richer and more precise pose stylization. Moreover, we develop a scalable data engine to construct a large-scale dataset of "Image-Skeleton-Mesh" triplets, enabling the model to jointly learn identity preservation and geometric alignment. Extensive experiments demonstrate that PoseMaster significantly outperforms state-of-the-art methods in both qualitative and quantitative metrics. Owing to the strict spatial alignment between the generated 3D meshes and the conditioning skeletons, PoseMaster enables the direct creation of animatable assets when coupled with automated skinning models, highlighting its compelling potential for automated character rigging.