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controlnet-pose

Generates pose-conditioned images or video motion transfers using RunComfy CLI and JSON inputs.

12|2|Updated May 18, 2026
Search Tags:#image generation#controlnet#depth map#pose conditioning#video pose transfer#openpose#comfyui workflows
One-click install
npx skills add https://github.com/runcomfy-com/skills --skill controlnet-pose
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: controlnet-pose
Source: https://github.com/runcomfy-com/skills/tree/main/controlnet-pose
Command: npx skills add https://github.com/runcomfy-com/skills --skill controlnet-pose

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

ControlNet pose references are hard to translate into consistent image or video generation without manually choosing the right model endpoint and workflow.

Core Features & Use Cases

  • Pose-conditioned routing: Routes automatically between video pose-transfer and image pose-conditioned generation based on whether the user provides a reference video vs a pose/control image.
  • Multiple supported pose modalities: Handles motion/pose transfer using Kling motion control tiers and image conditioning using Z-Image ControlNet LoRA with control images like OpenPose skeletons, DWPose, canny edges, or depth maps.
  • Practical outputs for creatives: Enables transferring a reference performance’s motion/blocking onto a target character or locking a character to a specific stance for photoreal or stylized results.

Quick Start

Use the controlnet-pose skill to generate pose-conditioned results by providing either a reference_video_url and character_image_url for motion transfer, or a prompt plus a control_image_url for control-image conditioning.

Frequently Asked Questions about controlnet-pose

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I use ControlNet pose references for image generation?▼

Video pose transfer applies a reference performance's motion onto a target character by providing a reference video URL and character image URL to Kling motion control endpoints. This replicates the reference video's blocking exactly.

Can I use OpenPose skeletons and canny edges for pose conditioning?▼

Yes, pose conditioning supports multiple control image modalities including OpenPose skeletons, DWPose, canny edges, and depth maps. The system routes these control images to apply structural constraints during the image generation process.

What inputs do I need for video pose transfer?▼

Video pose transfer requires a reference video URL containing the target motion and a character image URL defining the target subject. These inputs route through Kling motion control tiers to generate the transferred motion output.

Does pose transfer work with ComfyUI workflows?▼

Yes, pose-conditioned generation executes through structured RunComfy CLI invocations using JSON input parameters. It enforces a Bash runcomfy command allowlist for deterministic API-based generation within ComfyUI workflows.

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