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AdnMLDeformer

The AdnMLDeformer is a SOP deformer for Houdini that applies deformation driven by a trained Adonis ML model. It uses KineFX joint transforms as inputs for ML inference and applies the resulting deformation to the input geometry. The node also integrates mush smoothing to refine the inferred shape and provides paintable weights to control the influence of the inference, mush smoothing, and overall deformation per vertex.

To learn more about how to train an Adonis ML model, please check the Adonis ML Neural Training Tool page.

IMPORTANT
  • The AdnMLDeformer can be used with an FX license. However, an Adonis ML license is required to generate the Adonis ML model required by this deformer.

How To Use

The AdnMLDeformer requires three main inputs:

  • The geometry to apply the deformer to. This is the geometry deformed by a Bone Deform node.
  • The Adonis ML model file (.adnm).
  • The geostream containing the KineFX joints. This stream must include the name and localtransform per-point attributes.

Apart from the inputs required, there are also other aspects to be satisfied for this deformer to produce the expected results:

  • The Bone Deform node must exist in the deformable chain of the geometry to apply the AdnMLDeformer to.
  • The name of the Bone Deform node must follow a fixed naming convention: "<GEO_NAME>_bonedeform"
  • The input geometry and the Bone Deform node must be the same used in the data extraction process.
  • The deformation mode of the Bone Deform must be Linear.
  • The ADN_IN_ and ADN_OUT_ null nodes must be present and encapsulate the Bone Deform node for the creator and editor utils to work properly.
Initial state of the network to create AdnMLDeformer
Figure 1: Required setup of the network to create an AdnMLDeformer in Houdini.

To create and configure the deformer easily, there is a shortcut in the Adonis menu.

  1. Select the geometry to apply the deformer to, and then the node containing the KineFX joints.
  2. Go to the Adonis menu and click on ML Deformer.
  3. A simple UI will pop up to provide two inputs: ML Model File and Joints Info File.
Creator tool for AdnMLDeformer
Figure 2: Simple UI to easy the creation and configuration of AdnMLDeformer.
  1. Click on the Browse button of the ML Model File to provide the .adnm file.
  2. Click on the Browse button of the Joints Info File to provide the joints.json file. Make sure that both files are compatible with each other; that is, the joints.json file must be the one generated during the training process when the Adonis ML model was trained.
Creator tool for AdnMLDeformer with values
Figure 3: UI to create AdnMLDeformer with the model and joints files provided.
  1. Click on the Create button.
  2. The AdnMLDeformer will be created before the bonedeform node, if present, of the given geometry, and the joints found in the KineFX rig and present in the joints.json file will be populated in the "ML Inputs" tab of the node.
Network view after creating AdnMLDeformer
Figure 4: Network view after creating AdnMLDeformer.
  1. The deformer is ready. Tweak the envelope and/or enable or disable the inference to see the effect of the Adonis ML model.
NOTE
  • Creating the node manually is also possible in the Network View through Adonis > Deformer > AdnMLDeformer.
  • In that case, the ML Model path and the list of ML Inputs (KineFX joints) can also be populated afterwards from the same UI by selecting the AdnMLDeformer plus the geometry with the KineFX joints and launching Adonis > ML Deformer.
  • The use of this UI is recommended to ensure that the list of ML Inputs is consistent with the Adonis ML Model.
  • Machine learning dependencies are installed inside the Adonis installation directory rather than system-wide. As a result, the system environment remains unchanged and no global Python packages are installed.
  • (Windows only) ML inference will run on the GPU if the ML Dependencies have been previously installed. If not, the ML inference is performed on the CPU. Please, learn how to install the dependencies in this section on the Installation page.

The AdnMLDeformer integrates the mush algorithm to apply smoothing to the shape resulting from the inferred deformation. This algorithm requires a reference geometry to cache the displacements. Typically, the AdnMLDeformer is applied before the deformation chain; that is, the input source geometry is at rest, meaning that the input source is valid for mush data initialization. However, the AdnMLDeformer supports a second source to optionally ingest a custom rest shape for mush data initialization.

Attributes

ML Inference Attributes

Name Type Default Animatable Description
Enable Inference Boolean True ✓ Flag to enable or disable the inferred deformation.
ML Model Path String ✓ File path to the trained Adonis ML model.

Mush Properties

Name Type Default Animatable Description
Iterations Integer 10 ✓ Number of smoothing iterations applied by the mush algorithm. Greater values produce smoother results at the expense of additional computational cost. Has a range of [0, 20]. The upper limit is soft; higher values can be used.
Pin Boolean True ✓ Flag to pin the vertices on the boundaries.
Smoothing Step Float 0.5 ✓ Amount of smoothing applied at each iteration. Has a range of [0.0, 1.0].
Displacement Float 1.0 ✓ Controls how much of the computed displacement is applied to the geometry. Has a range of [0.0, 1.0].

Deformer Attributes

Name Type Default Animatable Description
Envelope Float 1.0 ✓ Specifies the deformation scale factor. Has a range of [0.0, 1.0]. The upper and lower limits are soft, and values can be set in a range of [-2.0, 2.0].

ML Inputs

Name Type Default Animatable Description
ML Inputs Root String ✓ Object path of the KineFX joint rig with the name and localtransform attributes required to retrieve the ML Inputs list.
ML Inputs List List 0 ✓ Number of inputs required by the Adonis ML model for inference.
ML Inputs String ✓ Name of the KineFX joint from which to read the localtransform attribute used as input to the Adonis ML model for inference.

Maps

Name Type Default Animatable Description
Inference Weights Attribute float 1.0 ✓ Specifies the name of the per-point attribute used to read the weight of the inferred deformation. The expected attribute name is adnInferenceWeights. The expected range of the per-component per-point values is [0.0, 1.0].
Mush Weights Attribute float 1.0 ✓ Specifies the name of the per-point attribute used to read the weight of the mush smoothing. The expected attribute name is adnMushWeights. The expected range of the per-component per-point values is [0.0, 1.0].
Weights Attribute float 1.0 ✓ Specifies the name of the per-point attribute used to read the weight of the deformation. The expected attribute name is adnWeights. The expected range of the per-component per-point values is [0.0, 1.0].

Parameter Template

ML deformer parameter template (settings tab)
Figure 5: AdnMLDeformer Parameter Template (Part 1): Settings.
ML deformer parameter template (ML inputs tab)
Figure 6: AdnMLDeformer Parameter Template (Part 2): ML Inputs.
ML deformer parameter template (maps tab)
Figure 7: AdnMLDeformer Parameter Template (Part 3): Maps.

Paintable Weights

Name Default Description
Inference Weights 1.0 Weights map used to control the influence of the inferred deformation at each vertex.
Mush Weights 1.0 Weights map used to control the influence of the mush deformation at each vertex.
Weights 1.0 Global weights map used to control the influence of the deformer at each vertex.
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