Synthetic Visual Genome 2: Extracting Large-scale Spatio-Temporal Scene Graphs from Videos
This work provides a significantly larger dataset and a more performant model for spatio-temporal scene graph generation, which is crucial for researchers developing advanced video understanding systems.
The authors introduce Synthetic Visual Genome 2 (SVG2), a new dataset of over 636K videos with 6.6M objects, 52.0M attributes, and 6.7M relations, representing an order-of-magnitude increase in scale for spatio-temporal scene graphs. They also developed TRaSER, a video scene graph generation model, which improves relation detection by 15-20%, object prediction by 30-40%, and attribute prediction by 15% over baselines, and boosts video question answering accuracy by 1.5-4.6% when used with a VLM.
We introduce Synthetic Visual Genome 2 (SVG2), a large-scale panoptic video scene graph dataset. SVG2 contains over 636K videos with 6.6M objects, 52.0M attributes, and 6.7M relations, providing an order-of-magnitude increase in scale and diversity over prior spatio-temporal scene graph datasets. To create SVG2, we design a fully automated pipeline that combines multi-scale panoptic segmentation, online-offline trajectory tracking with automatic new-object discovery, per-trajectory semantic parsing, and GPT-5-based spatio-temporal relation inference. Building on this resource, we train TRaSER, a video scene graph generation model. TRaSER augments VLMs with a trajectory-aligned token arrangement mechanism and new modules: an object-trajectory resampler and a temporal-window resampler to convert raw videos and panoptic trajectories into compact spatio-temporal scene graphs in a single forward pass. The temporal-window resampler binds visual tokens to short trajectory segments to preserve local motion and temporal semantics, while the object-trajectory resampler aggregates entire trajectories to maintain global context for objects. On the PVSG, VIPSeg, VidOR and SVG2 test datasets, TRaSER improves relation detection by +15 to 20%, object prediction by +30 to 40% over the strongest open-source baselines and by +13% over GPT-5, and attribute prediction by +15%. When TRaSER's generated scene graphs are sent to a VLM for video question answering, it delivers a +1.5 to 4.6% absolute accuracy gain over using video only or video augmented with Qwen2.5-VL's generated scene graphs, demonstrating the utility of explicit spatio-temporal scene graphs as an intermediate representation.