With the release of the Flame 2021.2 Update, four new training have been posted to the Flame Learning Channel. For the full list of enhancements, please also check out the Flame 2021 “What’s New”.
The first video covers a big enhancement to the user interface in terms of VFX and Grading. The new Single Panel View allows you to focus on the tool you have selected in an uncluttered display. Tools become easily accessible with fewer sub-menus and it also makes Flame easier to learn for new users. This is a global setting in your user profile for Action, Image and the Gmask Tracer, allowing you to work the way you want. The original Dual Panel View is still available for long time users that prefer that interface.
The second video focuses on a brand new workflow in Flame to quickly (and painlessly) build node flow graphs, even if you don’t know the names of the nodes!!! The new “Search…” functionality can be used with the node bins or you could bypass the node bins entirely to build any type of node flow graph. Set your favourite tools and hide the ones you don’t need. Flame is also smart enough to show you the tools you use the most when using “Search…” This welcome feature will accelerate your productivity and speed more than ever before!
The third video highlights our continued work with Machine Learning! Introducing the Salient Semantic Keyer! This amazing tool can identify objects within a bounding box based on its shape. So you can now create isolation mattes for objects based on the machine learning training and the picture quality (buildings, cars, boats, animals, people and more!) and use them within Image or Action for various grading and VFX tasks. Seeing is believing!
The last, but not least video, is a series of performance enhancements for Machine Learning and internally generated Motion Vectors in the Timeline. When using Machine Learning Models and generated Motion Vectors Maps, Flame will now cache the frames to disk to enhanced performance. This removes the need to recalculate the data analysis each time you visit the TimelineFX. Since this type of caching is now part of the project, it can now be managed as well as archived with the Flame project! There is also a new notification when Machine Learning models are initialising to let you know what Flame is doing. And finally, as a new CentOS only feature, the machine learning models are serialised or written to your system disk in order to load faster when you need them. To be clear, this affects the loading times for the machine learning algorithm and not the computations when analysing the footage with a machine learning model. Check out the video for the detailed explanations
You can check out these videos and more on the Flame Learning Channel - https://autode.sk/3kFxr9d
or in Autodesk Learning on AREA and as a downloadable podcast via iTunes.
Keep well and stay safe!