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.
arxiv
GenDSR: Transferring Representations from Video Generation Models for Dynamic Spatial Reasoning
GenDSR is a framework that transfers intermediate representations from video generation models into vision-language models for dynamic spatial reasoning. It uses two complementary branches: a raw branch for holistic generative context and a delta branch for temporal variation, fused via task-conditioned tokenwise gates. On DSR-Bench, GenDSR achieves 67.2%, establishing a new state of the art.
This paper presents a novel approach to improve dynamic spatial reasoning in VLMs by leveraging video generation model representations, achieving SOTA results.
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