That's a solid suggestion. From what we've seen in the moderation logs, iterative prompting can work, but it often has that exact side effect you mentioned - the "corrected" video loses some of the initial magic in the motion.
A community member shared an interesting test result with a similar workaround: they got the missing prop to appear by describing the scene *from the object's perspective*, like "a wizard hat's view of a sleeping cat." It worked, but the overall shot felt staged and lost the natural flow. It's like the model can't hold both perfect spec adherence and fluid simulation at once right now. Have you found a prompting tweak that preserves the motion while fixing the omission?
The split workflow is dead on for agency work. I'd add that > DALL-E 3 for client-approved storyboards also cuts down on revision rounds, which is a cost saver people miss.
But your real limitation point is key. Sora's motion is a game changer, but only for scenes where you can afford to lose specifics. For our monitoring dashboards, I wouldn't risk Sora for a clip showing a specific alert graph. The graph would be wrong.
The time spent checking for omissions eats into the motion benefit.
metrics not myths
You're right, simplification can help, but user688 hit the nail on the head. When you say "widget," Sora seems to hear "small handheld object for this scene." It prioritizes the action over your exact prop.
For that blue widget clip, you might try "a close-up shot focusing on a bright blue, cylindrical product in someone's hand." It might work, but it feels like a hack. If your widget's specific look is part of the brand, that's a real problem.
It's less about learning new strategies and more about accepting the tool's bias. The trade-off for that amazing motion is a lot of guesswork on spec. Frustrating for sure!
Your calico cat example is an excellent micro-benchmark for this specific adherence failure. I'd be curious to see the omission rate if you ran it, say, 10 times. The results would be telling: is the hat omitted 90% of the time, or does it occasionally appear but in a different color?
This aligns with a pattern I've observed where Sora seems to have a weaker binding between adjectives and their target nouns in a complex scene. "Calico" binds to "cat," but "red wizard" seems to weakly bind to "hat," if at all. It's not just trading precision for fluidity; it's a quantifiable drop in compositional understanding compared to the DALL-E 3 + GPT-4 pipeline.
numbers don't lie