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arXiv cs.GR
arXiv cs.GR Research
· 2 months, 2 weeks ago • Geonung Kim, Jeongeun Park, Nuri Ryu, Di Liu, Sunghyun Cho

SceneFrom3D: Geometry-Conditioned Outdoor 3D Scene Generation via View Scheduling with Object-Level Control

Briefing

SceneFrom3D is a geometry-conditioned outdoor scene generation framework aimed at one of the messiest parts of 3D synthesis: view scheduling. Instead of relying on a hand-authored or brittle sequence of camera positions, it automatically builds a directed generation graph from the input geometry. The graph uses anchor views and interpolation trajectories to decide what gets synthesized, which view pairs are bridged, and the order the generation proceeds in.

That matters because outdoor environments are large, unstructured, and effectively unbounded, which makes coverage hard to balance against stability. In practical terms, the system is trying to reduce the planning overhead that can make geometry-conditioned scene generation awkward to use in production workflows. For developers experimenting with AI-assisted environment creation, the interesting part is not just image quality, but the control structure around the generation process.

SceneFrom3D also adds object-level conditioning. Each object gets an identity image for appearance guidance, plus a geometry-adherence parameter that lets the system tune how tightly a region follows the source geometry. That gives artists and technical teams a more granular lever than scene-wide prompts or coarse constraints, especially when specific props or landmarks need to remain recognizable.

The framework is reported to reach state-of-the-art results for geometry-conditioned outdoor 3D scene generation, with controllable object appearance and geometry adherence. The broader implication is that scene generation is moving from...

“constructs a directed generation graph”

— SceneFrom3D framework · Core mechanism for automatic view scheduling
Original source
Read on arXiv cs.GR
At a glance
what
SceneFrom3D is a geometry-conditioned outdoor 3D scene generation framework with automatic view scheduling and object-level control.
who
The work is by Geonung Kim, Jeongeun Park, Nuri Ryu, Di Liu, and Sunghyun Cho.
when
Submitted to arXiv on July 5, 2026 (arXiv:2607.04540).
impact
It could reduce manual view-planning work and improve controllability in AI-assisted outdoor environment generation.
Signal Positive

Promising control and quality gains for scene generation

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