SPATIALALIGN: Aligning Dynamic Spatial Relationships in Video Generation

• URL: http://arxiv.org/abs/2602.22745v2
• Date: Fri, 27 Feb 2026 03:35:01 GMT
• Title: SPATIALALIGN: Aligning Dynamic Spatial Relationships in Video Generation
• Authors: Fengming Liu, Tat-Jen Cham, Chuanxia Zheng,
• Abstract summary: Most text-to-video (T2V) generators prioritize aesthetic quality, but often ignore the spatial constraints in the generated videos.<n>We present SPATIALALIGN, a self-improvement framework that enhances T2V models capabilities to depict Dynamic Spatial Relationships (DSR) specified in text prompts.<n>Specifically, we design DSR-SCORE, a geometry-based metric that quantitatively measures the alignment between generated videos and the specified DSRs in prompts.
• Score: 37.165709423088266
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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