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arXiv · 2602.22745

SPATIALALIGN: Aligning Dynamic Spatial Relationships in Video Generation

Fengming Liu·Tat-Jen Cham·Chuanxia Zheng
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Abstract

Most text-to-video (T2V) generators prioritize aesthetic quality, but often ignoring the spatial constraints in the generated videos. In this work, we present SPATIALALIGN, a self-improvement framework that enhances T2V models capabilities to depict Dynamic Spatial Relationships (DSR) specified in text prompts. We present a zeroth-order regularized Direct Preference Optimization (DPO) to fine-tune T2V models towards better alignment with DSR. Specifically, we design DSR-SCORE, a geometry-based metric that quantitatively measures the alignment between generated videos and the specified DSRs in prompts, which is a step forward from prior works that rely on VLM for evaluation. We also conduct a dataset of text-video pairs with diverse DSRs to facilitate the study. Extensive experiments demonstrate that our fine-tuned model significantly out performs the baseline in spatial relationships. The code will be released in Link. Project page: https://fengming001ntu.github.io/SpatialAlign/

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Fengming Liu, Tat-Jen Cham, Chuanxia Zheng. 2026-02-26. SPATIALALIGN: Aligning Dynamic Spatial Relationships in Video Generation. https://arxiv.org/abs/2602.22745

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