AI Video Development
Building a more controllable AI video workflow for SawCap.
This project was built around a practical production problem: pure text-to-video generation is still highly luck-dependent, and repeated retries can quickly drive token costs up. Instead of relying on chance, I built a workflow that increased control at each stage of the process.
Method
Why I did not rely on prompt-only video generation
At the moment, text-only AI video generation often depends too heavily on randomness. You can spend significant tokens iterating toward the right image logic, motion rhythm, and composition, only to end up with inconsistent outputs.
To reduce that uncertainty, I first used ChatGPT's image generation to produce the key frames that defined the visual direction of the video. Those frames then became the structural anchors for the motion stage.
I imported the key frames into Seedance 2.0 and used its first-frame / last-frame control together with descriptive prompt guidance to increase consistency and keep the generation aligned with the intended sequence. This made the workflow far more controllable than relying on open-ended prompt generation alone.
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Generated Video Outputs
AI motion studies refined through a controlled frame-based pipeline
Workflow
Adding post-production to remove the cheap AI feel
The AI generation stage was not the endpoint. After generating the motion outputs, I used Adobe Premiere Pro for additional editing, compositing decisions, element placement, and filter treatment.
That final layer was important because raw AI output can easily feel synthetic or visually cheap. Post-production let me recover texture, tighten pacing, and push the videos closer to a finish level that feels usable for marketing rather than just experimental.
The larger takeaway is that AI content workflows become much more valuable when they are treated as controlled hybrid systems, not as one-click generators. The combination of keyframe planning, video model control, and editorial finishing produces a better balance between efficiency and quality.