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What you can do with Mixamo LLM Mocap

A Python pipeline that turns a locked-camera video, filmed or AI-generated, into an FK animation on any Mixamo character, with scripted steps that an AI agent can run end to end.

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squall01337/mixamo-llm-mocap on GitHub

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What do you want to do?

4 use cases

  • Turn a locked-camera video into a Mixamo animation

    Retarget a single-performer video onto your Mixamo rig as a headless FK clip in Blender.

    View prompt
    In the squall01337/mixamo-llm-mocap repo, take the video at <video path> and an action spec for it, then run the SMPL-X path (estimate_pose_gvhmr.py --smplx-only, retarget_smplx.py, bl_motion.py -b apply) onto the rig <rig .blend path>. Expected output: a saved .blend with the action keyed on the Mixamo rig. Verify the .blend exists and the action name in the spec matches the action in the file.

  • Check a retargeted clip against its source video

    Render a side-by-side review and measure limb, head and foot errors against the plate.

    View prompt
    In the squall01337/mixamo-llm-mocap repo, run render_review.py and eval_fidelity.py on the clip at <clip folder> using its spec <spec path>. Expected output: a review video and a fidelity report with limb and head angles, foot slide and jitter. Verify the report lists foot slide and jitter values for the clip before you call it done.

  • Build a two-character fight scene from one video

    Split a two-performer plate into two Mixamo characters and check reach and collisions.

    View prompt
    In the squall01337/mixamo-llm-mocap repo, estimate both performers from the plate at <video path> with --person left and --person right, then run setup_duo.py and compare_pair.py. Expected output: one scene with both characters and a pair report on separation, reach and intrusion. Verify the pair report lists the frames where the fighters come closest and that no intrusion values are missing.

  • Have an AI agent run the landmark pipeline in Blender

    Drive the landmark path and live Blender apply through the Blender MCP socket.

    View prompt
    In the squall01337/mixamo-llm-mocap repo, with Blender open and the Blender MCP add-on enabled, run the landmark path on <video path>, writing the beat sheet from analyze_landmarks.py numbers. Expected output: preview.mp4 and showcase.mp4 in the clip folder. Verify both files exist and that compare_reference.py reports the frame windows that still differ.

How to use Mixamo LLM Mocap

  1. Check you have what it needs: Blender 5.1 or newer; A Mixamo character downloaded as FBX Binary in T-pose from mixamo.com; GVHMR cloned into tools/GVHMR with its checkpoints (about 5 GB), set up per docs/INSTALL.md.
  2. Open squall01337/mixamo-llm-mocap on GitHub and read the README and license.
  3. Build your rig scene from your Mixamo download: blender --background --python pipeline\setup_rig.py -- --fbx ybot.fbx --out ybot_rest.blend
  4. Write an action spec in action_specs\ with name, action_name, clip_dir, landmarks and fps (schema in docs/PIPELINE.md section S).
  5. Estimate the pose from the plate: tools\GVHMR\.venv\Scripts\python.exe pipeline\estimate_pose_gvhmr.py --video plates\<clip>.mp4 --out plates\<clip>\landmarks.json --smplx-only (inferred, check the README)
  6. Retarget the SMPL-X rotations onto your rig: python pipeline\retarget_smplx.py --spec action_specs\<clip>.json (inferred, check the README)

These steps come from our top use case, Turn a locked-camera video into a Mixamo animation, which also has a copy-paste agent prompt.

Mixamo LLM Mocap alternatives · Best AI repos for Content

About this repo

Requirements

  • Blender 5.1 or newer
  • A Mixamo character downloaded as FBX Binary in T-pose from mixamo.com
  • GVHMR cloned into tools/GVHMR with its checkpoints (about 5 GB), set up per docs/INSTALL.md
  • SMPL-X v1.1 body model (free research registration at smpl-x.is.tue.mpg.de), placed as SMPLX_NEUTRAL.npz
  • A GPU with about 8 GB VRAM (developed on an RTX 4080)
  • A locked-camera video plate (filmed or AI-generated)
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Questions about Mixamo LLM Mocap

What is Mixamo LLM Mocap?

A Python pipeline that turns a locked-camera video, filmed or AI-generated, into an FK animation on any Mixamo character, with scripted steps that an AI agent can run end to end. It is an open-source project on GitHub: https://github.com/squall01337/mixamo-llm-mocap.

Who is Mixamo LLM Mocap for?

Animators, game and content makers, and AI agent operators who need Mixamo-rig motion from video without a mocap suit or manual keyframing.

What can I do with Mixamo LLM Mocap?

4 use cases so far, for example: Turn a locked-camera video into a Mixamo animation; Check a retargeted clip against its source video; Build a two-character fight scene from one video. Each has steps and a copy-paste agent prompt.

What do I need to run Mixamo LLM Mocap?

Blender 5.1 or newer; A Mixamo character downloaded as FBX Binary in T-pose from mixamo.com; GVHMR cloned into tools/GVHMR with its checkpoints (about 5 GB), set up per docs/INSTALL.md; SMPL-X v1.1 body model (free research registration at smpl-x.is.tue.mpg.de), placed as SMPLX_NEUTRAL.npz.

What are alternatives to Mixamo LLM Mocap?

Other hot AI repos in Content: Voice Studio, Anatomy, Comfy UI, Hyperframes, Earshot.

Is Mixamo LLM Mocap safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.