Quick Summary
Baselight 7’s machine learning transform matching automatically rebuilds resizes from a ganged offline reference clip, handling keyframed moves, speed-affected shots, and stabilised plates through the Transform and Perspective operators – a conform rescue for XML or AAF deliveries that arrive without usable sizing data.
A real-world test on keyframes, speed effects, stabilised shots, and the one case that still belongs with VFX
You have been handed an offline with a lot of resizing in it, and an XML or AAF that either drops the transform information entirely or interprets it wrongly. Manually rebuilding those resizes from the reference burns hours, and it burns the kind of visual attention you really want to reserve for the actual grade.
Baselight 7 introduces machine learning transform matching. You gang a reference cursor to your conform, drop a Transform or Perspective operator at the bottom of the stack, and ask Baselight to “match all frames”. In this Insight, I put the feature through a gauntlet of real-world shots – plain resizes, keyframed moves, a speed effect, stabilised plates, and a picture-in-picture composite.
The results are excellent on most of the material. I also show you where the feature hits its limit, why that particular shot belongs with a compositor rather than a colourist, and a bonus technique for rebuilding a better camera stabilise from area-track data when you want to keep some of the original plate motion.
“Manually matching the resizes from a conform takes quite a lot of time, quite a lot of patience. It utilises all of your visual capabilities that you should be reserving for actually grading.”
Kali Bateman, Colourist
Key Takeaways
By the end of this Insight, you should understand how to:
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