GroundShot: Visually Consistent Multi-Shot Long Video Generation via Entity-Grounded Shot Scheduling

Researchers introduce GroundShot, a training-free framework for generating visually consistent multi-shot videos by maintaining entity-level memory and intelligently scheduling shot generation order. The method addresses a fundamental challenge in video generation where characters, objects, and locations drift in appearance across shots, and comes with GroundBench, a new diagnostic benchmark for measuring entity-level consistency.

GroundShot tackles a critical limitation in generative video AI: maintaining visual consistency across multiple shots. Traditional video generation models struggle as content accumulates, causing entities to change appearance unpredictably when they reappear. This framework solves the problem through a novel insight—viewers judge consistency by comparing later appearances against the first clear depiction of each entity. By prioritizing that initial appearance as a visual anchor, GroundShot establishes a consistency ceiling that subsequent generations must match.

The innovation operates as a training-free, model-agnostic system that works with existing video generation models without requiring retraining. This approach builds online entity memory from accepted shots, strategically schedules which shots generate first based on their value as reference points, verifies entity reliability before storing them, and retrieves appropriate references before generating new shots. The accompanying GroundBench benchmark represents a methodological advance, shifting consistency evaluation from global video metrics to entity-level granularity with controlled challenge dimensions.

This development matters significantly for the AI video generation industry, which continues advancing toward production-quality output. Visually consistent multi-shot videos are essential for creating cohesive narratives, advertisements, and creative content. The training-free nature removes implementation barriers—developers can integrate GroundShot into existing pipelines immediately. For enterprises deploying video generation, this reduces the computational and technical overhead typically required for model improvement. The entity-grounded approach also sets a new standard for how consistency should be measured and optimized in generative systems.

Future developments likely focus on scaling this framework to longer videos, optimizing memory efficiency, and adapting it for different entity types. Competition will intensify around consistency metrics, pushing other researchers toward entity-centric evaluation approaches.

• →GroundShot uses entity-level memory and shot scheduling to maintain visual consistency across multi-shot video generation without model retraining.
• →The framework establishes first appearances of entities as consistency anchors, preventing drift in character, object, and location depictions across shots.
• →GroundBench introduces a new diagnostic benchmark that evaluates consistency at the entity level rather than global video metrics.
• →The training-free, model-agnostic design enables integration with existing video generation models without requiring additional computational resources.
• →This work represents a methodological shift in how video generation consistency is measured and optimized within the AI industry.