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GeoFix提出了一种统一框架，通过单一几何奖励提升4D生成中的几何一致性和视觉质量，该奖励衡量静态区域的跨视角深度一致性，并针对视角重叠效应进行校准，实现了WorldScore 3D一致性提升39%和VBench质量提升4%。
Presents Self-Geometry, a plug-and-play test-time adaptation pipeline that enforces explicit multi-view geometric constraints using 2D pixel correspondences to improve geometrically consistent 3D vision foundation models.
ConsiSpace is a geometry-consistency-aware framework for video spatial reasoning that improves performance on spatial reasoning benchmarks by using a geometry-consistent memory and unified consistency self-supervised reinforcement learning.
MVTrack4Gen introduces a training framework that uses multi-view point tracking as geometric supervision to enhance motion-aware diffusion models, achieving state-of-the-art geometric consistency and motion fidelity in novel-view video generation from monocular video.
This paper proposes a self-supervised reinforcement learning framework that uses consistency verifiers—reward functions checking geometric and semantic consistency under transformations—to improve spatial reasoning in large reasoning models without requiring ground-truth annotations. The method approaches the accuracy of supervised fine-tuning and generalizes across diverse tasks.
A quantitative framework called PDI-Bench is introduced for evaluating geometric coherence in generated videos through monocular reconstruction and projective-geometry residuals, revealing geometry-specific failure modes in video generators.