- Updated: July 3, 2026
- 1 min read
GroundShot: Visually Consistent Multi‑Shot Long Video Generation via Entity‑Grounded Shot Scheduling
GroundShot: A Breakthrough in Multi‑Shot Video Consistency
Generating long videos that remain visually consistent across multiple shots is a longstanding challenge in computer vision. In the recent GroundShot framework, researchers introduce an entity‑grounded approach that builds an online visual memory of entities (characters, objects, locations) and schedules shot generation to maximise consistency.
Key Contributions
- Training‑free, model‑agnostic pipeline that works with existing generative video models.
- Entity‑level visual memory that stores the first clear appearance of each entity and re‑uses it as a reference for later shots.
- GroundBench benchmark for fine‑grained, entity‑level consistency evaluation.
The method dramatically reduces drift of recurring entities, setting a new consistency ceiling for multi‑shot video generation without any additional training.
Read more about the implementation and results on our internal blog: https://ubos.tech/groundshot.
For a visual overview, see the diagram below.
Authors: Yixuan Lai, Tianjia Shao, Kun Zhou, Weijia Dou, Siyu Zhu, Jingdong Wang.
Andrii Bidochko
CTO UBOS
Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.