Fine-grained spatial accuracy in generated images requires explicit spatial reward modeling during training; rule-based spatial checks alone miss complex relationships that vision-language models with grounding can catch.
SpatialReward is a reward model that helps text-to-image AI systems generate images with accurate object positioning and spatial relationships. It breaks down image prompts into specific spatial requirements, uses object detection to verify positions, and applies reasoning to check complex spatial relationships—then feeds this feedback into training to improve image generation quality.