Writing Transforms

Writing Transforms

When to Use

Use this skill when the user asks to move an object, set a transform, update position, animate a transform, or set a camera transform.

Inputs

Resolve inputs in this order: existing repository files and referenced snippets, explicit user request, then broader agent context.

• Target API surface: Python, C/C++, or both.
• Prim paths to move, desired translation/rotation/scale or full 4x4 matrices, and whether values are authored per prim.
• Transform storage name, semantic conversion, row-vector matrix convention, and sync/async behavior.
• Update cadence: one-shot movement, per-frame animation, or zero-copy GPU-computed transform updates.
• Repository source snippets referenced below. Treat these snippets as the API source of truth.

Prerequisites

• Use an ovrtx checkout that contains the referenced examples and docs tests.
• Read the relevant > **Source:** snippet before writing or explaining API usage.
• Use attribute-bindings for per-frame transform writes to the same prims, and mapping-attributes when GPU kernels should write transforms directly into mapped ovrtx buffers.

Instructions

1. Identify the target language, prim paths, transform representation, matrix convention, memory target, and sync/async requirement.
2. Read the matching source snippet and copy its lifecycle pattern rather than inventing equivalent calls.
3. Validate dtype, shape, semantic, and ownership rules before proposing or editing code.
4. Keep bindings, mapped buffers, operation handles, and C strings alive only for the lifetimes documented by the referenced examples.
5. When changing code, run the narrow docs test or example that owns the snippet whenever practical.

Output Format

• For explanations, cite the relevant API names, source snippets, and caveats.
• For code changes, summarize the files changed, snippets affected, and validation run.

Scripts

This skill has no scripts.

Limitations

• Transform semantic conversion is write-side only; read transform attributes with raw storage semantics.
• The referenced snippets remain the source of truth; update or add tested snippets before documenting new API usage.

Overview

Transforms control the position, rotation, and scale of prims in the scene. ovrtx supports three transform representations. The most common is a 4x4 matrix of doubles using the USD row-vector convention: translation is in the last row ([3][0..2]). Use the "omni:xform" attribute name, which is also used by the C convenience helpers in ovrtx_attributes.h.

Python

Write a 4x4 transform (single prim)

Source: tests/docs/python/test_attribute_shapes.py snippet doc-shape-mat4-array

Write transforms for multiple prims

Source: tests/docs/python/test_attribute_bindings.py snippet doc-bind-attribute-write

C

Write identity transform on a single prim

Source: examples/c/vulkan-interop/src/main.cpp snippet write-camera-transform

Batch write (N prims)

Source: examples/c/vulkan-interop/src/main.cpp snippet write-camera-transform

For multiple prims, increase prim_list.num_paths and provide a contiguous array of transforms.

C convenience helpers (ovrtx_attributes.h)

Instead of building DLTensor and binding descriptors manually, use the convenience helpers that handle the boilerplate:

Source: tests/docs/c/test_transform_helpers.cpp snippets doc-set-xform-mat-c, doc-set-xform-pos-rot-scale-c, doc-set-xform-pos-rot3x3-c, doc-set-reset-xform-stack-c.

#include <ovrtx/ovrtx_attributes.h> provides ovrtx_set_xform_mat(renderer, paths, count, transforms), ovrtx_set_xform_pos_rot_scale(...), ovrtx_set_xform_pos_rot3x3(...), and ovrtx_set_reset_xform_stack(...).

Tensor layout

Python and C expose different transform tensor layouts:

• Python uses NumPy-style matrices: an N-prim batch has shape (N, 4, 4) and dtype=float64.
• C uses DLTensor lanes for attributes: an N-prim batch has shape [N] and DLDataType{kDLFloat, 64, 16}.
• C rendered outputs/AOVs are different from attributes; image tensors use channel-last shapes such as [height, width, channels] with dtype.lanes=1.

Source: tests/docs/python/test_attribute_shapes.py snippet doc-shape-mat4-array.

Source: tests/docs/c/test_attribute_shapes.cpp snippet doc-shape-mat4-array-c.

Semantics are a write-side conversion hint. ovrtx_read_attribute rejects any semantic other than OVRTX_SEMANTIC_NONE with "Semantic conversion is not supported for read_attribute". When reading a transform attribute, leave the semantic as OVRTX_SEMANTIC_NONE and ask for the raw storage layout.

Key Types / Functions

C convenience helpers (#include <ovrtx/ovrtx_attributes.h>):

• ovrtx_set_xform_mat(renderer, paths, count, transforms) -- 4x4 matrix
• ovrtx_set_xform_pos_rot_scale(renderer, paths, count, transforms) -- pos + quat + scale
• ovrtx_set_xform_pos_rot3x3(renderer, paths, count, transforms) -- pos + 3x3 rotation
• ovrtx_set_reset_xform_stack(renderer, paths, count, values) -- set omni:resetXformStack

Python semantic: Semantic.XFORM_MAT4x4 (from from ovrtx import Semantic). For interop, the preferred approach is dtype="float64", shape=(4, 4) on bind_attribute/map_attribute, or passing an (N, 4, 4) float64 array directly to write_attribute (type is auto-inferred when the attribute already exists in the stage). The dtype parameter accepts string names ("float64"), NumPy dtypes (np.float64), or Python built-ins (float). The semantic= parameter is required when creating attributes from scratch via prim_mode=PrimMode.CREATE_NEW.

Troubleshooting

• Matrices use the USD row-vector convention (same as GfMatrix4d). Translation is in the last row: v[12], v[13], v[14] (or matrix[3][0..2] in Python/C).
• Use the "omni:xform" attribute name to match the C convenience helpers (ovrtx_set_xform_mat, etc.).
• Transform dtype is float64 (doubles), not float32. Using the wrong dtype will cause errors.
• For repeated per-frame transform updates, use attribute bindings or mapping for better performance (see attribute-bindings and mapping-attributes skills).
• Earlier code may use ovrtx.math.Matrix4d for single-prim writes; the ovrtx.math module and the Matrix4d class have been removed. Pass an (N, 4, 4) float64 array (NumPy, Warp, or any DLPack-compatible tensor) directly to write_attribute, or use dtype="float64", shape=(4, 4) with bind_attribute / map_attribute.

References

• Use the > **Source:** directives in this skill to locate tested snippets before reusing API patterns.
• Keep related skills, docs, and snippets synchronized when changing the workflow.