Human-in-the-Loop Atlas-Based 3D Asset Segmentation for Interactive Content Workflows
For interactive media and game developers, this work provides a practical segmentation pipeline with user control, though it is incremental and limited to specific domain objects.
The paper presents a human-in-the-loop pipeline that uses SAM~2 and Label Studio to segment 3D assets into a 2D parameterized atlas, enabling interactive segmentation for content workflows. Evaluated on eight cultural heritage objects, the approach generates usable atlases but requires manual correction for fine structures, cavities, and weak boundaries.
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.