This is a deep dive into testing for AI video models! In this series, We’ve designed a set of 10 carefully crafted prompts that serve as a quick and effective gauge of any video model’s capabilities. Each prompt is scored out of 1 point—allocating 0.5 for visual adherence, which measures how accurately the model renders the visual details, and another 0.5 for overall performance, reflecting how well it interprets and executes the prompt’s intent.
This approach provides a straightforward yet insightful way to assess different models. Whether you’re a developer fine-tuning your AI or simply curious about the latest in video generation, these benchmarks will help you quickly compare performance and uncover the strengths and limitations of each model.
🔹 Motion & Physics Accuracy
✅ These test whether the model understands real-world movement, weight, and force application.
- A lumberjack cutting down a tree → Force application & object deformation
- A carpenter hammers a nail into a piece of wood → Precision in small-object interaction & cause/effect realism
- A ball bounces down some steps → Gravity simulation, trajectory prediction, bounce physics
- A ball swinging on a string → Pendulum physics & smooth motion tracking
💡 If these fail: The model doesn’t apply correct physics, motion may be jittery, and objects might float or pass through each other unrealistically.
🔹 Action & Combat Simulation
✅ These test how well the model understands dynamic movement & multi-character interactions.
- 2 ninjas fighting with katanas → Fast combat tracking, collision physics, & motion consistency
- A man fires a pistol → Gun recoil, muzzle flash rendering, projectile motion tracking
- A ballerina doing a 360-degree spin → Smooth rotational motion & character limb positioning
💡 If these fail: The AI misaligns movement, doesn’t track cause & effect, or breaks animation flow (e.g., ballerina’s arms shifting unnaturally mid-spin).
🔹 Object Rendering & Consistency
✅ These test how well the model recognizes objects, maintains their details, and prevents distortion over frames.
- Cherry juice being poured into a glass from a jug → Liquid physics, transparency, and motion tracking
- A shop sign with the text “This is Ai” → Text rendering & spatial accuracy
💡 If these fail: The AI distorts objects, misaligns them, or struggles with text consistency across frames (which is a known issue in many AI models).
🔹 Facial Recognition & Realism
✅ These test how well the model handles known personalities and facial structure.
- Will Smith eating spaghetti → Celebrity likeness accuracy, hand-to-mouth motion, & object interaction
💡 If these fail: The model may morph facial features, struggle with food-to-mouth movement, or generate hands incorrectly (common AI issue).
Conclusion
In conclusion, these benchmark tests provide a clear window into the inner workings and potential of AI video models. By assessing key areas—motion and physics accuracy, dynamic action sequences, object consistency, and facial recognition—you gain a comprehensive understanding of a model’s strengths and shortcomings. A high score across these prompts indicates a model that not only pays close attention to visual details but also accurately interprets real-world dynamics and interactions.
If a model struggles with any of these tests, it signals areas ripe for improvement, whether that’s in simulating gravity, maintaining object integrity, or accurately capturing human expressions. Ultimately, these benchmarks serve as a practical and scalable method for developers, researchers, and enthusiasts to quickly gauge and compare the performance of any video model, driving forward the evolution of more reliable and realistic AI-generated content.
Embrace these tests as a roadmap for both evaluating current capabilities and inspiring future enhancements in the realm of AI video generation. And for the record, the model that scores highest on these tests as of the writing of this post is Wan 2.1 from Alibaba with a score of 8/10
