Prompting an AI video model to pour liquid or drape fabric produces mixed results. Sometimes the output looks like a high-budget commercial; other times, the liquid defies gravity or morphs through glass.
Understanding why physical interactions succeed or fail comes down to how diffusion models predict movement.
How AI Video Handles Physics
AI video models do not run real-time physics engines like game engines or VFX renderers. Instead, they predict frame-by-frame pixels based on statistical patterns in their training data.
- Implicit Pattern Matching: When simulating common movements (waves crashing, cars driving), the prediction is highly accurate.
- Micro-Interaction Guessing: Complex micro-interactions (fluid turbulence, fabric folding under tension, splash droplets) rely on probabilistic guessing that can break under scrutiny.
Model Strengths Across Physical Materials
| Material Behavior | Veo 3.1 | Kling 3.0 | Runway Gen-4.5 |
|---|---|---|---|
| Liquid Pours & Splashes | High photorealism & light refraction | Strong fluid dynamics & splash detail | Solid, best on mid-distance pours |
| Fabric Drape & Secondary Motion | Natural photorealistic bias | Convincing flutter & swirl motion | Class-leading cloth movement with body physics |
| Rigid-Body Collisions | Minimal morphing on long clips | High particle & impact accuracy | Strong inertia and weight simulation |
For details on physics benchmark testing, read Hailuo AI: speed and physics simulation.
3 Known Failure Modes in Food & Product Shots
- Object Permanence Breakdown: Items (garnishes, ice cubes, packaging labels) can subtly shift shape or position across 5+ second generations.
- Text & Label Corruption: On-screen brand names and nutrition labels frequently render garbled or illegible in macro close-ups.
- Temporal Degradation Past 20 Seconds: Continuous single-pass generations tend to lose physical coherence past 20 seconds.
Read our overview on what professional-grade means across AI video tools.
4 Rules for Prompting Physical Materials
- Use Wide/Medium Framing: Wide environmental shots give models statistical margin for error, whereas macro close-ups expose minor artifacts.
- Overlay Packaging Text in Post: Add crisp product logos and typography during video editing rather than baking text into the prompt.
- Generate Short 6-Second Cuts: Keep individual renders short to maintain physical stability, then assemble them in an NLE.
- Stress-Test Prompts Across Models: Run identical prompts across Veo 3.1, Kling 3.0, and Hailuo to compare fluid realism before selecting the final engine.
Read why we use Hailuo for rapid iteration and a different tool for final delivery.
Summary
AI video tools deliver convincing physical motion for wide shots and standard liquid pours, but struggle with macro label legibility and continuous long takes. Pairing AI generation with practical post-production editing ensures high-quality commercial assets.
Want to evaluate AI physics simulation for your product catalog? Get a free consultation to review test renders.
FAQs
Can AI video tools accurately generate liquid pouring?
Yes, particularly in wide or medium framing. Veo 3.1 and Kling 3.0 lead in fluid dynamics and light refraction through liquid.
Which AI generator is best for fabric simulation?
Kling 3.0 and Runway Gen-4.5 excel at fabric motion, with Kling delivering strong secondary flutter and Runway maintaining structural consistency.
Why does packaging text look distorted in AI videos?
Diffusion models generate images probabilistically rather than rendering vector typography, causing text and logos to distort in close-ups.
Should product videos be generated or shot practically?
Environmental B-roll and lifestyle shots perform well with AI generation. Macro close-ups requiring precise text legibility benefit from practical photography or hybrid editing.