CGCircuit – The Fastest SkinCluster Export/Import

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Info:
The Fastest SkinCluster Export/Import- A Studio Workflow: The Art of Compression.

Are you a Maya Technical Artist dying to learn the Industry secrets and Workflows used in the Top-tier Visual Effects Studios? Or maybe an Individual or Student looking to improve your Scripting, Development, and Workflow Planning skills?

In this Training Series we’ll learn how to write a Maya Python Deformer Importer/Exporter that is 40 times faster and outputs a file 60 times smaller than the conventional workflow/code that’s used at some of the highest level VFX Studios.

9 out of 10 Studios Save SkinCluster weights in the Same, Slow, Outdated fashion, and we’re going to Change That!

My name is Noah Schnapp and I’m the Head of Rigging and CFX at my current studio and I’m also a VFX Workflow/Pipeline Consultant, and I’m happy to share with you the industry techniques I’ve learned and developed over the years.

We will cover the secret to this incredibly powerful and versatile Compression Algorithm and Workflow. Not only will we examine Deformer data manipulation and extreme compression ratio strategies, we will also open a world of creative coding opportunities to your current and future toolsets. You will never think about development planning and writing code the same way again.

Thousands of dollars are spent on improving large, complex tasks, but the cost of the frequently run, smaller tasks are the most taxing to a studio’s success. I’ve worked at a variety of studios over my career and the main common flaw were core tools used by multiple Artists that were scripted in the same, slow method implemented more than 10 years ago.

Why aren’t Studios talking about this? It’s a case of “If it’s not broken, don’t fix it”. We all have seen the classic SkinCluster exporters, but this technology does not survive in our world today. We have higher polycounts, more joints, and more characters to rig. If we cut down on the costs of the frequent short tasks, we will save much more time and money rather than focusing our efforts on the rarely used large tasks.

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