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How to build a two-character fight scene from one video with Mixamo LLM Mocap

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

  • Content
  • Advanced
  • 6 hours
  • From the README
  • Any agent

Who it is forA choreographer or game cinematic artist with one video of two performers who wants both fighters on separate Mixamo characters in one scene.

The problemKeying two fighters by hand is slow, and retargeted fighters can clip through each other because Mixamo characters are thicker than the performers.

The outcomeOne Blender scene with both characters, plus a pair comparison of separation, strike reach and limb intrusion against the video.

What you need first

  • 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)

Steps

  1. Estimate each fighter from the plate with the landmark path: tools\GVHMR\.venv\Scripts\python.exe pipeline\estimate_pose_gvhmr.py --video plates\<clip>.mp4 --out plates\<clip>\landmarks.json --person left (repeat with --person right) (inferred, check the README)
  2. Run analyze_landmarks.py on each landmarks file, then write one action spec per fighter from the printed numbers.
  3. Lift and check each fighter: tools\GVHMR\.venv\Scripts\python.exe pipeline\lift_to_mixamo.py --spec action_specs\<fighter>.json, then tools\GVHMR\.venv\Scripts\python.exe pipeline\qa_clip.py --spec action_specs\<fighter>.json (inferred, check the README)
  4. Build the shared scene with setup_duo.py, then compare the pair: tools\GVHMR\.venv\Scripts\python.exe pipeline\compare_pair.py (inferred, check the README)
  5. Run the Blender collision pass on the skinned meshes: python pipeline\run_in_blender.py all action_specs\<fighter>.json (inferred, check the README)

Prompt for your agent

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.

Caveat. The README notes that clearance is bought with a declared, measured offset from the stage, so faithful retargets of close exchanges may need that offset and the cost stays visible in the comparator.

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