Do moral values align between images and the fictional stories about them, within human narratives and between humans and large language models (LLMs)? This question matters because stories are central to how humans communicate moral values, yet little is known about how people and LLMs perform this task in a multimodal (text and image) setting. We present a systematic comparison of moral values represented in human- and LLM-generated narratives based on images annotated by humans for moral content. Our analysis shows that while human stories reflect a balanced distribution of moral foundations and coherent narrative arcs, LLMs disproportionately emphasize the Care foundation and often lack emotional resolution. Even with moral conditioning, these biases persist. We introduce a novel dataset and framework for evaluating moral storytelling in vision-language models, highlighting key challenges in aligning AI with human moral reasoning across cultures.