Generative View Stitching

Generative View Stitching (GVS) addresses a critical bottleneck in current video generation: the 'myopia' of autoregressive models. While models like SVD or Gen-2 can generate short clips, long-range camera movements often fail because the model creates geometry in early frames that it later 'collides' with or forgets. GVS's proposal of parallel sampling for global consistency is technically sound but faces extreme competition. With 0 stars and 5 forks just 8 days after release, it is currently in a 'pre-traction' research phase. The moat is purely algorithmic; there is no proprietary dataset or network effect. Frontier labs (OpenAI with Sora, Luma AI, and Runway) are already tackling global consistency using similar transformer-based 'world model' approaches or global attention mechanisms. Because this is a sampling-time optimization, it is highly likely to be absorbed as a standard technique in larger proprietary pipelines within 6 months, rendering a standalone project obsolete. The high platform domination risk reflects that camera-guided control is a primary feature roadmap item for every major video foundation model provider.