AI Yoga Video Production for yogatailor

GenAI Yoga Videos Production-Ready for the yogatailor Web App

yogatailor is a web app that builds a personalised 21-session yoga program around better sleep, more flexibility or less stress. It has no streaks and no badges, so the videos carry the whole experience. The client had about 50 AI-generated yoga videos, roughly 80 minutes in total, and they were not ready for real users: the model changed from shot to shot, the camera zoomed in and out for no reason, and the background shifted mid-clip. I took them from rough generations to consistent, production-quality videos. The app is now live, people are engaging with the content, and viewers can’t tell it’s AI.

The brief

The client had generated about 50 videos with Kling, WAN and motion control, and each one had its own funny glitches and weird zooms. They needed someone who could clean them up so the whole library felt like one production: the same person in every video, no unexpected camera movement, the same background and consistent body proportions. For each video I received two files: a reference video shot with a real human model and the AI-generated version of it.

This mattered more than it would for a single ad. A user follows a 21-session program, so they see the same instructor again and again. If the face, the body or the room changes between sessions, the app stops feeling like a personal program and starts feeling like a pile of clips.

What I delivered

I produced the full library of 50+ yoga videos for the yogatailor web app, about 80 minutes in total. Every video shows the same instructor in the same setting, with a steady camera and accurate poses from the first movement to the last. The library includes sleep, flexibility and stress sessions and mixes simple movements with complex ones.

Yoga is demanding for AI video, because the movements are detailed: limbs cross, bend and hold for long stretches. I built the videos to stay accurate through those moments.

How I worked

Reference first

Every video was built from a reference clip of a real model. I used it as the source of truth for movement and timing, so each pose is accurate and the pacing matches what a real instructor would do, including the complex poses.

One consistent instructor

I mapped the reference movement onto a single AI avatar using Kling motion control. All 50+ videos share the same face, body and proportions, so the instructor looks like one person from the first session to the last.

Steady camera, steady background

Each clip has a fixed camera and the same background, with no zooms or shifts, so nothing on screen moves except the person. That makes it easy to follow along with a pose.

Quality checks and iteration

I checked each video against its reference for proportions, motion and consistency, and reworked anything that wasn’t right. I worked with the client’s feedback throughout and kept iterating until each video was approved.

The result

About 80 minutes of video across 50+ clips is now live in the app. The instructor looks the same in every session, the camera stays put and the background doesn’t change. People are engaging with the content, and they can’t tell it’s AI. For a product built on trust and routine, that is what matters: the video disappears and the practice stays.

What the client said:

She helped us create 50+ videos of yoga content. Some of this content is difficult with complex and detailed movements. She was patient and always open to working on feedback and re-iterating.

Need consistent AI video for your product?

I produce AI video that stays consistent from the first frame to the last: a steady identity, a steady camera and believable motion. If you need a library of videos that looks like one production, send me your reference material and I’ll tell you how I’d approach it and how long it would take.