想精确控制视频里多个物体的互动？GraphVid用交互图代替画轨迹，效果比Motion-I2V好很多，FID降了40%。
GraphVid是一种基于图结构的图像到视频生成模型，通过交互图实现多对象精准控制。研究团队构建了GraphVid-Bench数据集，包含结构化关系标注。与Motion-I2V相比，GraphVid的FID降低39.9%，FVD降低37.6%，PSNR从9.87提升至15.98，SSIM从0.38提升至0.61。该方法使用更少训练数据和参数，在可控性和质量上均表现更优。
GraphVid: Interactive Graph-Controllable Video Generation
Controllable video generation remains challenging due to the difficulty of specifying precise multi-object interactions using text prompts or motion-control inputs that primarily constrain pixel movement. In practice, trajectory-based control often requires users to draw accurate tracks for multiple objects, which scales poorly with scene complexity and becomes ambiguous under occlusion or overlap. To enable flexible yet precise multi-subject control, we introduce $\textbf{GraphVid}$, a graph-conditioned image-to-video generation model that enables interactive control through structured interaction graphs. We further curate $\textbf{GraphVid-Bench}$, a large-scale interaction-centric video dataset with structured relational annotations to enable training of interaction-aware video generation models. Despite using substantially less training data and fewer trainable parameters than prior motion-control methods, GraphVid delivers strong controllability and video quality. Compared with Motion-I2V, GraphVid reduces FID by up to 39.9% and FVD by 37.6%, while improving PSNR (9.87=>15.98) and SSIM (0.38=>0.61). Our results highlight the potential of structured semantic interfaces as a powerful paradigm for controllable video generation.