[Dry Goods] AI Video Generation Tutorial: 5 Core Tips to Completely Fix Logic Glitches and Character Deformation, Say Goodbye to 'Eyes Understand, Hands Fail'

There are countless tutorials on AI video generation online, but most either confuse you with parameters or only give a few empty incantations. When you actually try it yourself, you find the generated videos are either logically glitchy or have deformed characters, completely unusable.

If you're also in the state of 'eyes understand after watching tutorials, but hands fail when you try,' this short article clearly lists the most important points. No theoretical bombardment, just the issues that really stall you.

When Learning AI Video Generation, First Focus on These 5 Core Points

1. Prompt structure is more important than fancy words
   Many people immediately write 'cyberpunk, neon lights, rainy night, realistic,' and end up with nothing but light pollution. A good prompt follows 'subject + action + environment + lighting + camera language.' For example: a woman in a red raincoat walking quickly in an old Shanghai alleyway in the rain, the cat on her shoulder also tucks its neck, a front medium shot, the streetlight hits the raindrops creating a Tyndall effect. Try it, the result is completely different.
2. Parameter pitfalls: when generating with Sora, higher frame rate and stylization are not always better
   Some platforms (including professional tools like getsora2) offer multiple control dimensions. If you max out stylization or creativity, don't get too excited — the visuals may be beautiful, but motion coherence will drop sharply. If you want to generate continuous actions like 'a person stands up from a chair, picks up a glass, and drinks water,' keeping stylization between 4-5 is safer, otherwise the glass might simply pass through the hand.
3. Test prompts on multiple platforms
   The same prompt behaves very differently across different models. For instance, if you write 'a dog running on grass,' in Sora the grass might be rendered extremely finely but the dog's fur slightly blurry, while on another engine it might be the opposite. The truly efficient approach is to take a set of standardized prompts (3-5), batch run them on an aggregation platform like getsora2, pick the most stable one, and then fine-tune.
4. Controlling character consistency is harder than imagined
   Many people making short videos find it most frustrating: the same character has an oval face one second and a round face the next. There's no perfect solution yet, but there is a combination approach: fix the character's seed value + put the appearance description at the very front of the prompt + keep video length within 5 seconds. Longer than 5 seconds, the character will likely 'drift.' Short clips edited together are actually more reliable.
5. Post-processing adjustments are more important than generation
   AI video raw output often has issues like fingers moving randomly, background flickering, objects changing size suddenly. When you get the material, don't rush to release it. First denoise frames, then use tools like Topaz to add details. Even just simply cutting off the first 3 frames and last 2 frames (the start and end frames are most prone to breaking) can significantly improve the final visual quality.

A Practical Scenario That Is Often Overlooked

Creators making product demo videos often encounter an awkward situation: hand-operated content is extremely difficult for AI to handle. For example, 'fingers sliding on a phone screen' or 'hand holding a screwdriver to turn a screw.' I've observed that many people have to re-shoot after generating such material. It is recommended to explicitly specify 'still objects,' 'minimalist background,' and 'slow motion' during the prompt preparation stage — that's a practical fallback.

How to Tell If a Tool or Tutorial Is Right for You

Don't look at how flashy its demo clips are; instead ask yourself:

• How long does it take to generate a 5-second 1080p clip? If it exceeds 3 minutes, unless the quality is exceptional, it's hard to do batch content.
• Can it directly export transparent channels or alpha-format files? This can save you a lot of time in post-production compositing.
• When you throw complex scenes at it, like multi-person interaction (two people shaking hands), does the result break down? If even the official examples only show single-person solo scenes, then the ceiling is obvious.

The value of aggregation platforms like getsora2 is that you don't have to stick to a single model; you can seamlessly switch between different tasks. Choose a model with strong rendering for landscape shorts, and one with good consistency for character close-ups — this is a pragmatic strategy at the current stage.

Finally, let me say: AI video generation is still far from the 'one-click output' stage. Instead of chasing tutorials everywhere, spend time understanding the limits of a single toolset. Every ten minutes you invest in debugging will be directly reflected in the quality of your final footage.