Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.23543v1</id>\n    <title>Synthetic Visual Genome 2: Extracting Large-scale Spatio-Temporal Scene Graphs from Videos</title>\n    <updated>2026-02-26T22:52:43Z</updated>\n    <link href='https://arxiv.org/abs/2602.23543v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.23543v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>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.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <published>2026-02-26T22:52:43Z</published>\n    <arxiv:primary_category term='cs.CV'/>\n    <author>\n      <name>Ziqi Gao</name>\n    </author>\n    <author>\n      <name>Jieyu Zhang</name>\n    </author>\n    <author>\n      <name>Wisdom Oluchi Ikezogwo</name>\n    </author>\n    <author>\n      <name>Jae Sung Park</name>\n    </author>\n    <author>\n      <name>Tario G. You</name>\n    </author>\n    <author>\n      <name>Daniel Ogbu</name>\n    </author>\n    <author>\n      <name>Chenhao Zheng</name>\n    </author>\n    <author>\n      <name>Weikai Huang</name>\n    </author>\n    <author>\n      <name>Yinuo Yang</name>\n    </author>\n    <author>\n      <name>Winson Han</name>\n    </author>\n    <author>\n      <name>Quan Kong</name>\n    </author>\n    <author>\n      <name>Rajat Saini</name>\n    </author>\n    <author>\n      <name>Ranjay Krishna</name>\n    </author>\n  </entry>"
}