Previously, I tried using video tools to automatically generate footage, and most results were frustrating — either character movements were jerky or scene logic was inconsistent. After trying several platforms repeatedly, I found that text to video AI indeed varies in quality. Today, I will directly review the actual performance across several common scenarios, no hype, no bias.
1. Comprehension of Text Descriptions: "Word-for-Word Translation" or "Scene Recreation"?
Most tools can only recognize keywords. For example, when inputting "a person running by the sea at dusk," the resulting footage is often a static seaside with a human silhouette pasted on — no light changes, no dynamic sense of running. But good models integrate the three elements "dusk," "seaside," and "running" into a coherent action. Here I have to mention sora, which is indeed a step ahead in interpreting complex scenes, especially the coherence of light and object interaction; few comparable tools exist at the moment.
2. Generation Duration and Resolution: Don't Just Watch Demos
Many promotional videos only show clips under 10 seconds, which look stunning, but there are pitfalls in actual use:
- Short clips (1-5 seconds): Most platforms can achieve 720p or higher, with decent detail retention
- Medium length (10-15 seconds): Many tools start to experience quality drops, flickering objects, especially on human faces
- Long clips (30 seconds+): Currently only a few can maintain stable quality, with resolution generally dropping below 540p
If you need to create product demos or short video ads, it's recommended to test continuous output of more than 10 seconds — don't be fooled by the first 5 seconds.
3. Motion Logic: Can It Maintain Physical Common Sense?
A common failure point: sudden texture changes when a character turns, a cup placed on a table but no reflection on the surface, water flowing in the wrong direction. Good text to video AI tools include a large amount of physical motion samples in their training data, rather than relying solely on static image interpolation. In actual comparison, very few models can handle "occlusion relationships between objects" — most experience clipping or disappearance when objects intersect.
4. Computing Power and Waiting Time: True Productivity or "Waiting Overnight"?
Running models locally requires extremely high-end GPUs. Many one-click generation solutions recommended by bloggers actually require a 40-minute queue to get a 5-second video. The speed of online platforms also varies greatly: some queue for an hour during peak times, others can produce a video in 5 minutes. If you need to generate more than 20 clips of material daily, prioritize platforms with GPU cluster scheduling and clearly marked "estimated waiting time."
5. Practical Trade-offs for Applicable Scenarios
Not all content is suitable for AI video generation. Based on my testing experience:
- Suitable: Visualization of abstract concepts, rapid prototyping, social media short videos, background materials (e.g., starry sky, smoke, ripples)
- Not suitable: Replacement of actual product footage, multi-person dialogue scenes, shots requiring precise control of expressions and gestures, core images for brand promotional videos
Especially for lip sync and hand gestures, currently all text to video AI tools cannot produce stable output without reference video.
6. What to Watch Next
If you want to start using it now, I recommend a "short + fast" combination: use AI to generate 3-5 second transition shots or atmosphere shots, while keeping core footage as live-action or traditional animation. Wait until models become more mature in handling physical motion (refer to the update direction of sora), then gradually expand the usage proportion. text to video AI is currently a "useful auxiliary tool," still a long way from being a "fully automatic director."
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