When a single line of text asks the five characters in a video to fight, hand things over, and dodge obstacles at the same time, today's generation models still tend to "each do their own thing". GraphVid's answer isn't more prompts — it's drawing the scene as an interaction graph first. The paper proposes a graph-conditioned image-to-video model that structures the objects, the actions, and the relationships between them, then hands the structured representation to the generator. A companion GraphVid-Bench is built, with relationship-annotated videos used to train and evaluate multi-subject interaction. The results are direct: versus Motion-I2V, FID drops by up to 39.9%, FVD drops by 37.6%, PSNR climbs from 9.87 to 15.98, and SSIM climbs from 0.38 to 0.61 — all with less training data and fewer trainable parameters. The value isn't just sharper frames; it's making "who influences whom" a computable condition for the model. In occlusion, overlap, and multi-subject coordination — the cases hardest to describe in text — a graph structure reads more like a director's storyboard than a long prompt. More importantly, this interface pushes video generation from "describing a picture" to "orchestrating relationships": future ads, game cutscenes, and robot training data may all start by editing a scene graph, then letting the model fill in the continuous frames. The paper is still a preprint, the metrics depend on the dataset and experimental setup, and it's still far from a general-purpose production tool. But the direction is clear: the next stage of video generation isn't about whether it can move — it's about whether it can move according to a structure.