The companion episode: one image becomes a rigged, animated 3D character, failures included.

Your AI character exists as beautiful 2D images. Here’s how we turn those images into rigged, animated 3D models, with a wardrobe of outfits, using a pipeline that runs entirely in the cloud while your GPU does something else.

The pipeline

Character LoRA (identity) → full-body "plate" image → image-to-3D
   → remesh → auto-rig → animations → GLB in the browser

Ours is fully scripted end-to-end: a new outfit goes from text prompt to animated character in the web app without opening a single 3D tool.

Step 1, A rig-friendly plate image

3D generators and auto-riggers want a very specific input, and this is 80% of your success:

  • Full body, head to feet, cut-off feet become melted geometry
  • T-pose (or A-pose), facing camera, riggers assume it
  • Plain, seamless background, no props, no scenery
  • Moderate proportions, extreme stylization survives, but hip-hugging poses and overlapping limbs don’t

If your character has a trained LoRA (we use one on Civitai (referral link, supports the channel at no extra cost) and generate plates via its cloud API), identity stays locked while you swap outfits per prompt: "…wearing an elegant fitted pink and gold qipao, T-pose, full body, plain grey studio background." Any consistent-character workflow works.

Step 2, image → 3D with Meshy

We feed the plate to Meshy via its REST API (image-to-3d): textured mesh out in ~2 minutes, T-posed and surprisingly faithful: hair color gradients, outfit embroidery, the works. It even re-poses non-T-pose inputs reasonably well, but a true T-pose plate gives the cleanest result.

Step 3, the gotcha: remesh before rigging

Auto-rigging caps at 300k faces, and detailed outfits blow past it (our qipao gown came out at 311k). The fix is one extra API call, remesh to ~150k, which also halves the file size for the web. We bake this into the pipeline unconditionally: image-to-3d → remesh → rig.

Step 4, auto-rig + animate

The rigging API takes the remeshed model and returns a standard humanoid skeleton with skin weights (~30 seconds). From there, an animation library applies idle/talk/gesture clips onto the rig, we export one GLB per animation (idle.glb, talk.glb) and crossfade between them at runtime.

Pick your idle deliberately. Animation #1 in any library is usually a neutral “video-game stance.” We previewed a batch and chose a relaxed, feminine idle, it changed the character’s entire presence.

Step 5, into the browser (three.js)

const idle = await loader.loadAsync("looks/qipao/idle.glb");
scene.add(idle.scene);
mixer = new THREE.AnimationMixer(idle.scene);
mixer.clipAction(idle.animations[0]).play();
// load talk.glb, steal its clip onto the same mixer, crossfade on speech

We organize outfits as a look library, one folder per look (source image, mesh, rigged, idle, talk, thumb) plus a JSON manifest, so the web app’s outfit picker populates itself. A brand-new outfit is: generate plate → run pipeline → done; it appears in the dropdown automatically.

Honest limitations

  • No facial rig. Auto-rigged meshes have a body skeleton but no jaw bone or face blendshapes, mouths don’t move. Real lip-sync needs a face-capable avatar (VRM or a character-creation suite); that’s its own guide (coming soon).
  • Likeness is “very good,” not pixel-perfect, faces read correctly at video-call distance; extreme close-ups reveal texture softness.

See the outfit switcher live in Aillex’s videos on YouTube → @AskAillex. Related: the full architecture.

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