Segmenting 3D assets into meaningful regions remains challenging, especially when segmentation criteria are application-dependent and require user control. We present a human-in-the-loop pipeline for generating a segmented 2D parameterized atlas from a 3D model for interactive media, game, and XR content workflows. Our method first selects a compact set of rendered views using a greedy set cover strategy over sampled surface points, and then supports interactive segmentation of these views with SAM~2 and Label Studio. The resulting masks are back-projected onto the model's UV parameterization to produce a unified segmented atlas that supports downstream production tasks such as segment-wise material assignment, style transfer, and semantic labeling. We assess the pipeline through a demonstration-based technical evaluation on eight cultural heritage objects. The results show that the approach can generate usable segmented atlases across diverse geometries while revealing recurring sources of manual correction, particularly fine structures, cavities, and weak appearance boundaries.

翻译：将三维资产分割为有意义的区域仍具挑战性，尤其是当分割标准依赖应用场景且需要用户控制时。我们提出了一种面向交互式媒体、游戏及扩展现实内容工作流的人机协同流水线，可从三维模型生成已分割的二维参数化图谱。该方法首先通过基于采样表面点的贪心集合覆盖策略选取紧凑的渲染视图集，随后利用SAM~2和Label Studio对这些视图进行交互式分割。生成的分割掩码通过逆投影映射至模型的UV参数化空间，形成统一的带参分割图谱，支持材质按区域分配、风格迁移及语义标注等下游生产任务。我们以八件文化遗产对象为案例，通过示范性技术评估对流水线进行验证。结果表明，该方法能在多样化几何体上生成可用的带参分割图谱，同时揭示了反复出现的人工修正需求，主要涉及精细结构、空腔及弱外观边界等区域。