graph-conditioned

GraphVid introduces a graph-conditioned image-to-video generation model that enables interactive control through structured interaction graphs, outperforming prior methods with significant reductions in FID and FVD.

Proposes GC-MoE, a graph-conditioned mixture of experts framework for traffic forecasting that assigns each node a personalized combination of frozen pretrained spatio-temporal GNN experts based on graph topology and recent input, training only a lightweight routing module (∼17K parameters) and achieving competitive performance on four benchmarks.