Houdini 21.0 Nodes Geometry nodes

APEX Add ML Deformer geometry node

Procedurally add a ML Deformer to an APEX rig

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Overview

This node edits an APEX rig to apply effects from the ML Deformer to the posed character.

This node can use a trained ML model to deform the skin of a character. Internally, the ML model predicts weights to combine the residual blend shapes to apply a correction to the rest skin.

Note

APEX Add ML Deformer assumes the model was trained on a data set that was created with the help of ML Pose Serialize using its default settings. These settings are Mode set to Subskeleton and Include World Rotation turned on. Also, Joint Group needs to match the joint group used with ML Pose Serialize to create the training data set. These requirements would be met if the ML Train Deformer recipe was used for training.

Inputs

CharacterStream

An APEX Packed Character to insert ML-driven Deformation.

Blend Shapes

The blend shapes used in combination with the weights output by the ML model to adapt the rest mesh. The first blend shape is always applied with a fixed weight of 1, the remaining blend shapes weighted by the outputs of the ML model, in the same order.

Parameters

Model File

The trained model in ONNX format that inputs a serialized pose and outputs a tuple of weights for the residual blend poses.

Execution Provider

Determines which ONNX execution provider to use for inference. By default, the node attempts to pick the best available provider and prefers to use GPU acceleration.

Automatic

Chooses the best provider for the current system. This option prioritizes CUDA if installed, DirectML/CoreML as a fallback depending on the platform, and CPU inference if no GPU provider is available.

CPU

Perform inference using on the CPU.

CUDA

Perform inference using CUDA/cuDNN. CUDA and cuDNN must be installed using the packages provided by NVIDIA.

DirectML

Only available on Windows. Performs inference using the Windows Direct Machine Learning library.

CoreML

Only available on macOS. Performs inference using Apple’s Core ML library.

Joint Group

The joint group to serialize the pose for inclusion into the data set on which Model File was trained.

Enforce Joint Limits

When on, then the specified joint limits are applied to a pose before it goes into the model.

Joint Limits

Specify the name of a dictionary attribute that contains joint limits.

Blend Shapes Source

Packed Residual

The Blend Shapes input is expected to consists of packed primitives. Each packed primitive is expected to contain a residual blend shape, which is a blend shape that has the capture shape subtracted from it.

Direct

Directly use the blend shapes provided on the Blend Shapes Input

See also

Geometry nodes