Load Pose Json v5.1.0

Load Pose Json turns a DWpose JSON file into a picture you can actually look at. The pack's pipeline works with pose files - height, width, and an array of keypoints - and a JSON on its own tells you nothing visually. This node reads one from ComfyUI's input folder and renders the skeleton: it draws the body pose (torso, arms, legs, face keypoints) and both hands on a black canvas at the JSON's native resolution, then returns that as a normal IMAGE. What you get is the wireframe version of the pose, ready to preview or feed onward.

The mechanism is direct. It lists files in ComfyUI's input/ directory, gives you a file-picker dropdown with upload enabled, and if the file you pick ends in .json, it loads it, allocates a blank canvas at the pose's height/width, and calls the pack's comfy_utils draw functions - draw_bodypose for the main body plus draw_handpose for the two hand keypoint ranges. If the file doesn't end in .json, it just returns the blank canvas (a vestigial path from when it was a general file loader).

Input and output

• image - a file picker, not an image wire. Pick (or upload) your pose JSON from the dropdown. The brief shows a single default sample (example.png) but any file in input/ appears, including your JSONs.
• Output: one IMAGE - the rendered skeleton.

Why it's useful

Three real reasons to keep it around. Debugging is the big one: when a pose-driven workflow produces a contorted or empty result, the fastest way to check whether the JSON itself is garbage is to render it and look. It's also a lightweight preview before you send the pose into a ControlNet openpose-style stage, and it doubles as a way to inspect which keypoints a detector actually found - if hands are missing, the draw_handpose region will be empty and you'll see it instantly. Compared to running a full pose preprocessor just to eyeball a file, this is nearly free.

Install and gotchas

Standard pack install:

cd ComfyUI/custom_nodes
git clone https://github.com/TRI3D-LC/tri3d-comfyui-nodes
# restart ComfyUI

Or ComfyUI Manager, search "tri3d." Keep the folder named tri3d-comfyui-nodes. Pure numpy + the pack's own draw utils - no model, no extra deps.

• It reads from the input folder, so your JSON has to be dropped there (or uploaded via the node) before it shows up in the picker.
• The rendered skeleton uses the JSON's own height/width as canvas size, which may not match the source image you associated with it. That's expected - the skeleton is supposed to be scaled later when it's consumed - but it means the preview isn't overlaid on the photo.
• It only renders keypoints that have valid coordinates; negative (undetected) keypoints produce no marks. A sparse JSON renders a sparse skeleton - which is exactly the diagnostic signal you want.

It's a viewer, and a good one for the job. If you spend any time feeding pose JSONs around this pack, this is the node that tells you whether your pose is beautiful or broken before you waste a generation on it.