OrbitForge用文本直接生成360度3D场景，解决了视频视角不全的问题，效果比单用MedianGS好很多。
OrbitForge利用冻结的视频先验和逐提示高斯泼溅重建优化，将单个文本生成视频转换为规范闭环轨道3D高斯泼溅场景。它通过可变形高斯泼溅和稳健MedianGS代理获得初步3D重建，然后渲染指定轨道视图检测缺失视角。该方法仅补全缺失视角并重建最终场景，无需任务特定视频或多视角微调。在300提示T3Bench审计中，OrbitForge达到了359.0度中位数跨度，并将Q10 ImageReward从8.07提升至16.36，同时与VideoMV保持竞争力。
OrbitForge: Text-to-3D Scene Generation via Reconstruction-Anchored Video Synthesis
Generic text-to-video models can be used as rich open-world scene priors. Despite the high quality of today's generated videos, they do not directly yield reliable 3D assets: camera motion is difficult to control, view coverage is partial, and frames often contain inconsistencies across time. We introduce OrbitForge, an adapter built from frozen video priors and per-prompt Gaussian Splatting reconstruction optimization that converts a single text-generated video into a canonical closed-orbit 3D Gaussian Splatting scene. We use 3D reconstruction as an anchor to improve the 3D consistency of the generated video. We obtain a preliminary 3D reconstruction from a first generated video via Deformable Gaussian Splatting with a robust MedianGS proxy. We render views from a prescribed orbit to detect missing viewpoints. OrbitForge uses the text-to-video model to complete only the missing views, and reconstructs the completed orbit into a final Gaussian Splatting scene. This design requires no task-specific video or multiview fine-tuning, avoids per-prompt score-distillation optimization, and does not progressively generate views one step at a time. We further argue that this setting demands coverage-aware evaluation: local smoothness alone rewards methods that never attempt a full orbit. On a frozen 300-prompt T3Bench-derived audit, OrbitForge reconstruction attains a 359.0-degree measured median span, raises originally unsupported-bin Q10 ImageReward from 8.07 to 16.36 relative to MedianGS-only reconstruction, while remaining competitive with VideoMV on the coverage-quality.