Migrate MPPCA and finalize numpy migration with Python bindings #967
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary: Completed migration of parameter transform functions in the geometry module from torch.Tensor to numpy arrays. This maintains consistency with the geometry module's design principle of using numpy for fast forward operations while keeping torch tensors in diff_geometry for differentiable operations. Key changes: - Implemented arrayToParameterSet() to convert boolean numpy arrays to ParameterSet - Updated all parameter property bindings (scaling_parameters, rigid_parameters, etc.) to return numpy arrays - Converted simplify(), simplify_parameter_transform(), parameters_for_joints(), and joints_for_parameters() to use arrays - Updated reduce_to_selected_model_parameters() to use array-based parameter sets - Updated tests to work with numpy arrays instead of tensors - Updated .pyi type stubs to reflect numpy array usage Differential Revision: D89891109
Summary: Implemented numpy array version of apply_model_param_limits() for the geometry module. This function clamps model parameters to their specified min/max bounds using the character's parameter limits. The implementation uses ModelParametersAccessor for clean data access and supports arbitrary batch dimensions with parallel processing. It only applies MinMax type parameter limits. Key changes: - Added applyModelParameterLimits() in momentum_geometry.cpp/h - Uses ArrayChecker pattern for input validation - Supports both float32 and float64 dtypes - Parallel batch processing with dispenso - Bound as Character.apply_model_param_limits() - Added tests in test_geometry.py - Added consistency test in test_geometry_diff_geometry_consistency.py - Updated .pyi type stubs Differential Revision: D89891111
Summary: Converted Character.skin_skinned_locators() from torch.Tensor to numpy arrays to maintain consistency with the geometry module. This function applies linear blend skinning to compute world-space positions of skinned locators. The implementation follows the core skinning algorithm from momentum's skinned_locator_error_function: for each locator, transform the rest position using inverse bind pose and joint transforms weighted by bone influences. Key changes: - Added skinSkinnedLocatorsArray() in array_skinning.cpp/h - Uses SkeletonStateAccessor and VectorArrayAccessor for clean data access - Supports arbitrary batch dimensions with parallel processing - Handles optional rest positions (uses locator positions if not provided) - Returns empty [0, 3] array if character has no skinned locators - Updated Character.skin_skinned_locators() binding to use array version - Updated .pyi type stubs Differential Revision: D89891114
Summary: Migrated find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh from torch tensors to numpy arrays. This continues the broader migration of pymomentum geometry operations from PyTorch to numpy. The key technical challenge was ensuring correct memory layout for the SimdKdTree library. Eigen matrices use column-major storage by default, but SimdKdTree expects row-major data where each point's coordinates are contiguous. Added a conditional toMatrix() method to VectorArrayAccessor that uses RowMajor storage for multi-dimensional points (Dim > 1) while preserving ColMajor for column vectors (Dim == 1) as required by Eigen. Differential Revision: D89891110
Summary: Pull Request resolved: #967 Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features. Key changes: - Implemented array_mppca for converting MPPCA models to numpy arrays - Changed Mppca.to_tensors() to Mppca.to_arrays() returning numpy arrays instead of torch tensors - Added Python bindings for find_closest_points, find_closest_points_with_normals, and find_closest_points_on_mesh using numpy arrays - Removed dependency on ATen/PyTorch tensor headers from geometry module - Updated test_posePrior to use numpy array indexing instead of tensor.select() Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes). Reviewed By: jeongseok-meta Differential Revision: D89891113
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Summary:
Completed the migration from PyTorch tensors to numpy arrays for both MPPCA (Mixture of Probabilistic PCA) operations and kd-tree function bindings. This commit provides the Python bindings for both features.
Key changes:
Note: This commit includes bindings for both the kd-tree functions (from the previous commit) and MPPCA, as they share common infrastructure changes (removing tensor includes and adding array includes).
Reviewed By: jeongseok-meta
Differential Revision: D89891113