GenDSR: Transferring Representations from Video Generation Models for Dynamic Spatial Reasoning
· arxiv.org · popularity 6/100 · 0 similar stories today · Machine Learning Research (Artificial Intelligence)
Read the original at arxiv.org →
Summary
arXiv:2610.41314v1 Announce Type: cross Abstract: Dynamic spatial reasoning is essential for connecting visual intelligence to thephysical world, yet remains challenging for vision-language models (VLMs), which must preserve scene state while tracking how spatial relations evolve over time. Meanwhile, video generation models (VGMs), trained to synthesize temporally coherent world dynamics, offer a rich source of spatiotemporal representations. We present GenDSR, a framework that transfers intermediate representations from VGM into VLM for dynamic spatial reasoning. Rather than treating a VGM feature as a monolithic prior, GenDSR constructs two complementary branches: a raw branch preserving holistic generative context and a delta branch emphasizing temporal variation. Task-conditioned tokenwise gates then fuse both signals and inject the result into the native visual representation. On DSR-Bench, GenDSR achieves 67.2%, surpassing the previous best result and establishing a new state of the art.
Found in: ArXiv CS.AI. Rating basis: single outlet; outlet tier 2 (unlisted); 1 feeds; feed position 145.