Plan-and-Verify Video Reward Reasoning with Spatio-Temporal Scene Graph Grounding
Reward models for text-to-video (T2V) generation guide post-training but often fail at fine-grained semantic alignment. We trace this to two structural weaknesses in existing reasoning-based reward models: they do not systematically verify every condition described in the prompt, and the visual evidence supporting each judgment remains implicit in their free-form reasoning. We propose SG-PVR, a video reward model that addresses these limitations through plan-and-verify reasoning grounded in spatio-temporal scene graphs. The verification plan decomposes the prompt into atomic claims, making the set of requirements to be checked explicit. The spatio-temporal scene graph, encoding entities, ...
| Authors | Hyomin Kim, Junghye Kim, Joanie Hayoun Chung et al. |
|---|---|
| Primary category | cs.CV |
| Published | 10 June 2026 |
| Last updated at source | 28 September 2026 |
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