Boosting Text-Driven Video Segmentation via Geometry-Aware Distillation

Text-driven Referring Video Object Segmentation (RVOS)aims to locate and segment target objects in videos given natural lan-guage. However, existing models are typically trained on 2D image orvideo datasets with naive segmentation losses, which overlooks the geo-metric consistency across frames and leads to weak spatial understand-ing. In this paper, we propose Geometry-enhanced Language-guidedVideo segmentation (GeoLaV), a two-stage framework that distills 3Dgeometric knowledge from images to enhance text-driven video segmenta-tion. In the first stage, we perform monocular geometry pretraining withmonocular novel-view synthesis, enabling the model to acquire geometry-consistent visual representations via spatial alignment on large-scale single-image datasets. In the second stage, we introduce geometry-aware distil-lation and fine-tune the model on video segmentation datasets, transfer-ring 3D structural knowledge from a general 3D prior model. This processreinforces 3D awareness and improves both spatiotemporal coherence andlanguage grounding in segmentation. Extensive experiments show thatour method using only image segmentation data already provides notablezero-shot generalization in RVOS. When combined with geometry-awaredistillation for fine-tuning on videos, our method achieves state-of-the-artperformance across multiple RVOS benchmarks. The code is available athttps://github.com/Tony1882880/GeoLaV.