GeoFlow: Enforcing Implicit Geometric Consistency in Video Generation

TL;DR

This paper introduces a geometry-consistency reward for video generation that explicitly enforces geometric coherence, reducing artifacts and improving temporal consistency in generated videos.

Contribution

It proposes a novel, model-agnostic reward based on optical flow and depth-pose predictions to explicitly optimize geometric consistency in video generation.

Findings

Significant reduction in geometric artifacts compared to baselines.

Improved temporal consistency in generated videos.

Applicable to diverse dynamic scenes with camera and object motion.

Abstract

Generating geometrically consistent videos remains an open challenge: text-to-video diffusion models trained on web-scale data treat geometry only implicitly, leading to object deformation, texture drift, and non-rigid backgrounds under camera motion. Existing solutions either improve consistency as a byproduct, apply only to static scenes or realign the latent space of the model completely. We introduce a geometry-consistency reward that directly measures whether motion in a generated video is compatible with a coherent scene. Our key insight is that in physically consistent videos, background motion should be explainable by rigid camera-induced flow, while independently moving objects should preserve appearance identity along motion trajectories. We operationalize this using optical flow, depth--pose predictions, and feature-based correspondence to separate rigid and dynamic regions and evaluate their respective consistency. Integrating this reward with reinforcement fine-tuning transforms geometric consistency from an emergent property into an explicit optimization objective for video generators. The approach is model agnostic and applies to diverse dynamic scenes containing both camera and object motion. Experiments show substantial reductions in temporal geometric artifacts over strong baselines while preserving perceptual quality. Code and model weights are published.