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Adonis 2.1: Machine Learning Meets Digital Anatomy

Introducing AdonisML, a brand-new version of Adonis that gives animators high-fidelity skin deformation at interactive speeds - plus a major round of new features for AdonisFX.


Adonis 2.1: Machine Learning Meets Digital Anatomy

Introducing AdonisML, a brand-new version of Adonis that gives animators high-fidelity skin deformation at interactive speeds - plus a major round of new features for AdonisFX.


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Two products, one framework

From 2.1, Adonis comes in two flavours. AdonisFX is the digital anatomy toolset you already know - the muscle, fat, and skin simulation system that runs natively in Maya and Houdini. AdonisML is a new product that includes everything in AdonisFX, plus two new - and complementary - machine-learning systems: the AdonisML Deformer and SmartTissue. With these new capabilities, Adonis now provides an ML-powered path for animators that need high-fidelity results at interactive speed.

Introducing ML Deformer & SmartTissue

The ML Deformer gives animators interactive, high-quality representations of their character as they work. It’s trained on AdonisFX characters and uses a custom neural network training system that works quickly and efficiently to build the pose-based ML deformer. Working interactively with a high-quality asset means more iterations in the same amount of time, making for happy animators and great outcomes. 

This is great to a point, but ML deformers all have a similar limitation: the nature of the training means that all the interesting dynamics on a character are lost. Animators need that information in order to make the right creative choices as they work, which requires them to wait around for simulation playblasts. This is why we invented SmartTissue.

SmartTissue

SmartTissue is the dynamics layer that adds back what pose-based approaches lose - all the jiggle, settle, and overshoot that come from simulating fascia and fat. All the detail that brings a character to life and drives a great performance. 

SmartTissue uses our proprietary neural network architecture to predict how the underlying muscles are activating to achieve the current pose, then uses that to drive the simulation. The result is consistent, reliable, high-fidelity deformation without the computational expense of a full anatomy sim. When the character moves, all the dynamics you'd expect are solved rapidly - and crucially, you don't need to wrestle with physical parameters to get there. AdonisML gives you a set of controls to define SmartTissue's behaviour directly.

Built for the way animators actually work

Iterating effectively means seeing a playblast with all the dynamics in place, but waiting on a full simulation to render that playblast is exactly what slows a shot down. SmartTissue can be cached out, so playblasts are available to animators immediately. In most cases that cached output is good enough that an entire shot with multiple simulated creatures can be turned around to lighting and clients in a matter of minutes.

The ML training pipeline is fast and adaptive. In our test, a human character was trained from a standard range-of-motion - not from the animation you see in the clip - using 1,200 samples drawn from around 9,000 frames. Sample extraction took roughly six hours; training took roughly six minutes on an RTX 3090 / Ryzen 9 5950X.

What's new in AdonisFX

Alongside the launch of AdonisML, AdonisFX gets a significant update centred on a complete, topology-independent anatomy transfer system.

Anatomy transfer: the complete system

Most vertebrates share the same structural logic - a horse's hindlimb and a human leg have the same bones, the same muscle groups, the same tendon paths. The proportions and topology differ, but the underlying structure is similar, and that's what makes anatomy transfer possible.

AdnTransfer provides a complete workflow for moving anatomy geometry from one character to another by mapping the anatomical correspondence between them. Two new deformers sit at the core:

  • AdnRigidWrap attaches a mesh to one or more target meshes. Each point locks to the closest location on the targets and follows their animation, preserving its initial offset throughout.

  • AdnSoftWrap wraps a mesh around animated targets by blending influences from nearby areas within a defined radius. Where RigidWrap produces precise attachment, SoftWrap handles the tissue in between, producing continuous soft deformation across the whole mesh.

The Landmark Tool gives you a dedicated UI for placing and configuring the landmark pairs between source and target meshes, with correspondence set visually, directly on the meshes.

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AdnMush: fine detail in every solver

The new AdnMush preserves fine anatomical surface detail like the ribs or pectoral muscle striations, through deformation while still reducing skinning and post-simulation artefacts. In Adonis 2.1 it runs inside every solver as part of the solve itself, so the output is cleaner without adding an extra node to the deformation stack. It remains available as a standalone post-process node for shot-level corrections where you need to work without re-running the simulation.

For artists using the Turbo auto-setup workflow, the generated rig now uses AdnMush for artefact correction. A skin cluster that would previously have needed significant cleanup comes out of Turbo looking substantially better, so the gap between your starting point and producing something for initial feedback from a supe is smaller than it's ever been.

Get Adonis 2.1 today

Adonis 2.1 is available now.

  • AdonisFX - Pro licenses starting from £1,500 per year, Indie available

  • AdonisML - Pro licenses starting from £3,000 per year (contains everything in AdonisFX, plus the new AdonisML Deformer, SmartTissue, and related tools)

AdonisFX has a free 30-day trial - just download, install, and you're away. 

If you'd like to evaluate AdonisML, please get in touch with our team at [email protected].

Our Try | Buy page has more details on pricing and full documentation is available for the new features in the Adonis Docs.

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