geometric-consistency

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.