LoGo: Local-Global Rewards for Consistent Long-Horizon Video Generation
Camera-controlled video models are rapidly advancing toward long generation horizons and complex camera control. A key failure mode is 3D inconsistency: as the camera moves, objects lose permanence and scene structures shift. Existing post-training techniques, which assign a single scalar reward to the entire generation, are poorly suited to correcting these inconsistencies over long horizons. We…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
