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GenDSR: Transferring Representations from Video Generation Models for Dynamic Spatial Reasoning

2026-10-30 06:26:31
Ke Yang, Zhenyu Zhang, Jun Li
arXiv_CV
         
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.

Abstract (translated)

URL

https://arxiv.org/abs/2610.41314

PDF

https://arxiv.org/pdf/2610.41314.pdf

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