arXiv:2606.17824v1 Announce Type: cross Abstract: 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 ba
The proliferation of advanced AI models like SAM-2 and the increasing demand for high-quality 3D assets across various interactive content platforms are driving innovation in segmentation techniques.
This development allows for more efficient and customizable creation of 3D content, which is crucial for the scaling of interactive media, games, and extended reality applications.
The workflow for 3D asset segmentation becomes significantly more streamlined and user-controlled, reducing manual effort and improving the quality of segmented 3D models.
- · Game Developers
- · XR Content Creators
- · Interactive Media Studios
- · 3D Software Providers
- · Manual 3D Segmentation Service Providers (non-AI enhanced)
Faster and cheaper production of high-fidelity 3D assets for a variety of digital experiences.
Increased availability of realistic and segmented 3D models will accelerate the development and adoption of AR/VR and metaverse platforms.
The democratization of advanced 3D content creation tools could lead to a surge in user-generated interactive content and new digital economies.
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Read at arXiv cs.AI