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UniRig AI on Replicate predicts a skeleton and skinning weights for a 3D mesh, then returns a rigged model file. For a first try, upload a supported mesh in Replicate’s playground, run the model, download the result, and inspect its bones and deformations in a 3D application before using it in a project.

What UniRig AI does

Rigging gives a 3D model a structure that can be posed or animated. UniRig predicts two related parts: a skeleton, which defines the bones and their hierarchy, and skinning weights, which define how strongly each mesh vertex follows each bone. The official project treats skeleton prediction and skinning as separate stages, so the quality of the skeleton matters to the final deformation.

The Replicate listing for aaronjmars/unirig-ai presents a hosted model based on the UniRig framework. It accepts one mesh input and returns a file URI; the documented input formats are .glb, .obj, .fbx, and .vrm. The official example uses a GLB input and retrieves a downloadable GLB result. The output format shown by that example should not be taken as confirmation that every input format produces an identical result or preserves all source-file features.

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Try it in Replicate’s playground

  1. Prepare a mesh in one of the documented formats: .glb, .obj, .fbx, or .vrm.
  2. Open the UniRig AI model page and use its playground to provide the mesh as the input_mesh input.
  3. Start the run and wait for the result. Replicate describes runs as typically finishing within six minutes, but runtime varies with the input and is not a completion-time guarantee.
  4. Download the file URI returned by the run and open the result in a 3D application that can inspect its skeleton and skinning.

The model page lists A100 (80GB) hardware for hosted execution, so you do not need to configure a local GPU to use the playground. Replicate’s listing gives an approximate cost of $0.46 per run; the actual cost and runtime can vary with inputs. Check the live listing before running, since these are changeable estimates rather than fixed terms.

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Use the API if you need a repeatable workflow

Replicate documents API examples in Python, JavaScript, and HTTP. API access requires a Replicate token. The documented schema takes a single input_mesh URI and returns a file URI, which makes the API useful when you want to submit inputs and retrieve outputs from a script rather than work manually in the playground. Follow the examples and current input schema on the model API page; do not expose your token in public code or share it with others.

Inspect the rig before relying on it

A returned file is a prediction, not a guarantee of a production-ready rig. Open it in a 3D application and check that the skeleton covers the parts that need to move, then pose the model to see whether the mesh deforms as intended.

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  • Check skeleton completeness: look for missing or misplaced bones, especially on articulated features such as tails or wings.
  • Test deformation: move relevant joints and watch for collapsing, stretching, or parts of the mesh following the wrong bone.
  • Decide whether to refine: the UniRig repository warns that skinning can degrade significantly when the predicted skeleton is inaccurate. It recommends refining the skeleton before predicting skinning when needed.

For the local workflow, the repository distinguishes skeleton-only output from a skinning result and documents merging the result with the original mesh. That distinction is useful when diagnosing a poor result: a skeleton can be incomplete or misplaced before weights are even considered.

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Replicate or local installation?

Choice Setup and hardware Best fit
Replicate playground Upload a supported mesh and run the hosted model; Replicate lists A100 (80GB) hardware. The listing’s approximate $0.46-per-run cost and typical runtime within six minutes vary with inputs. Beginners who want to try a mesh without installing Python dependencies or managing a GPU.
Replicate API Uses the hosted model through documented Python, JavaScript, or HTTP examples; requires a Replicate token. Cost and runtime remain input-dependent. Users who want scripted submissions or to integrate the hosted model into a workflow.
Local UniRig project Requires Python 3.11, PyTorch 2.3.1 or later, project dependencies, and a CUDA-enabled GPU with at least 8GB VRAM for generation. The README warns that Flash Attention installation errors are common. Users comfortable configuring a Python and CUDA environment who want to run the documented project locally.

The local setup details and inference sequence are in the official UniRig repository. Optional VRM import and export requires installing a modified Blender VRM add-on described there. Local setup gives you responsibility for dependencies and hardware; it is not simply a no-cost version of the hosted route.

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What the hosted model represents

The official repository labels UniRig a SIGGRAPH 2025 project and cites the paper One Model to Rig Them All: Diverse Skeleton Rigging with UniRig, with ACM publication metadata dated 2025. The paper’s findings concern its evaluated setup; they do not establish a success rate or performance guarantee for every input sent through Replicate’s hosted wrapper.

The repository describes a progressive release: it lists skeleton and skinning prediction code and a checkpoint as available, while describing a full checkpoint that replicates the paper’s main results as planned. The hosted Replicate model is therefore best understood as a convenient way to try the listed model, not proof that every component or result of the complete research system is available in the wrapper.

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The repository links an MIT license for its code. That license alone does not establish the terms for every checkpoint, input asset, dataset, or generated output; check the relevant terms for the assets and components you use.

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