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Create pose ControlNet driver

create_pose_controlnet_driver

Creates an OpenPose stick-figure conditioning image from a pose tracking CHOP for ControlNet-driven Stable Diffusion, with optional Syphon/Spout or NDI output for downstream nodes.

Instructions

Render a canonical OpenPose-colored stick figure TOP (per-limb RGB lines + per-joint colored discs on a black background, default 512×512) from an existing pose CHOP produced by create_pose_tracking. The render is GPU-rasterized in a single GLSL TOP that samples the pose CHOP via a CHOP-to-TOP. Optionally auto-wires the output to a Syphon/Spout or NDI sender for a downstream Stable Diffusion / ComfyUI / StreamDiffusion ControlNet node. No model inference — this tool produces the driver conditioning image that ControlNet consumes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mirrorNoFlip horizontally (selfie cam vs. ControlNet expectation).
sourceNoWhere the pose stream comes from. 'existing_tracker' reads a 33-sample pose CHOP at pose_chop_path. 'synthetic' auto-spins-up a synthetic Script CHOP inside this container for device-free preview.existing_tracker
resolutionNoSquare render size. ControlNet SD1.5 wants 512; SDXL wants 768/1024.512
output_modeNoWhen 'internal' stops at a Null TOP. When 'syphon_spout'/'ndi' adds an FM-01 external sender.internal
parent_pathNoParent network for the pose_controlnet_driver baseCOMP./project1
sender_nameNoSender/source name advertised on the network when output_mode != 'internal'.tdmcp_controlnet_pose
color_presetNoCanonical OpenPose 18-keypoint COCO palette by default.openpose_coco
joint_radiusNoFilled-disc radius (px) for each keypoint joint. Exposed as live JointRadius knob.
limb_thicknessNoLine thickness (px) for each limb. Exposed as live LimbThickness knob.
pose_chop_pathNoRequired when source='existing_tracker'. Absolute TD path to the canonical 33-sample pose CHOP (tx/ty/tz/confidence).
confidence_gateNoSkip drawing landmarks/limbs whose endpoint confidence falls below this. Exposed as live knob.
expose_controlsNoExpose live JointRadius, LimbThickness, ConfidenceGate, Mirror knobs.
coordinate_spaceNoHow to map landmark tx/ty to pixel space. 'normalized' maps [-1,+1] to full square. 'world' recenters using hip_midpoint and auto-scales to body height.normalized
custom_limb_colorsNoWhen color_preset='custom'. Length must equal 17 (limb count).
custom_joint_colorsNoWhen color_preset='custom'. Length must equal 18 (joint count).
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With annotations already declaring it is not read-only and not destructive, the description adds valuable detail: GPU-rasterized via a single GLSL TOP, sampling via CHOP-to-TOP, optional auto-wiring to external senders, and the explicit clarification that no model inference occurs. It fully supplements the annotation without contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences and every sentence adds information: rendering output, internal mechanism, and optional output wiring. It is well-structured and front-loaded, though slightly longer than strictly necessary since some details (e.g., per-limb RGB lines, joint discs) are already evident from the title and parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (15 parameters, no output schema), the description is adequate: it explains what is produced (a TOP), how it receives data (pose CHOP), and its role in a ControlNet pipeline. It does not describe return values, but for a node-creating tool this is not a gap. The lack of an output schema is mitigated by the clear statement of the render output.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3 per the rubric. The description does not significantly add parameter-level meaning beyond the schema; it mentions the default 512×512 and the source from an existing pose CHOP, but these are already captured in the property descriptions. No additional compensation needed.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb 'Render' and clearly identifies the resource (canonical OpenPose-colored stick figure TOP) and purpose (driver conditioning image for ControlNet). It distinguishes itself from sibling tools like create_pose_tracking by stating it consumes the pose CHOP and explicitly says 'No model inference'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description places the tool in a pipeline: it takes a pose CHOP 'produced by create_pose_tracking' and feeds into 'a downstream Stable Diffusion / ComfyUI / StreamDiffusion ControlNet node'. This implies when to use it, though it does not explicitly name alternatives or when-not to use it, so it falls short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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