Title: Spatial Reasoning Network for Zero-shot Constrained Scene Generation. Abstract: Constrained scene generation (CSG) generates images satisfying a given set of constraints. Zero-shot CSG generates images satisfying constraints not presented in the training set without retraining. Recent neural-based models generate images with excellent details, but largely cannot satisfy constraints, especially in complex scenes involving multiple objects. Such difficulty is due to the lack of effective approaches combining low-level visual element generation with high-level spatial reasoning. We introduce a Spatial Reasoning Network for constrained scene generation (SPREN). SPREN adds to the state-of-the-art image generation networks (for low-level visual element generation) a spatial reasoning module (for high-level spatial reasoning). The spatial reasoning module decides objects' positions following the output of a Recursive Neural Network (RNN), which is trained to learn implicit spatial knowledge (such as trees growing from the ground) from an image dataset. During inference, explicit constraints can be enforced by a forward-checking algorithm, which blocks invalid decisions from the RNN in a zero-shot manner. In experiments, we demonstrate SPREN is able to generate images with excellent detail while satisfying complex spatial constraints. SPREN also transfers good quality scene generation to unseen constraints without retraining. 