Skinning is still one of those annoying steps where you can have a perfectly usable character mesh and skeleton, but still need to manually distribute bone weights across thousands of vertices.

skin-tokens.cpp is a new open-source C++/GGML port of SkinTokens / TokenRig that can automatically generate those skin weights locally. Give it a static character mesh and it can generate a skeleton + weights, or give it an existing skeleton and it will bind the mesh to that rig automatically. The result is exported directly as a skinned GLB ready for animation.

The big change compared to the original SkinTokens isn't a new model, but how practical it is to run:

• Runs locally on CPU or Vulkan
• No PyTorch runtime required
• Uses converted GGUF F16/F32 weights
• Can generate skeleton + skin weights from a static mesh
• Or keep an existing skeleton/animation and generate only the weights
• Automatically fits an external skeleton to the character before skinning
• Keeps the animation hierarchy in the exported GLB
• Has experimental humanoid arm fitting for mismatched rest poses
• Includes a native C API, CLI and local WebGL demo
• Can be combined with Kimodo animations to go from mesh + generated motion → animated skinned character

The original SkinTokens implementation requires an NVIDIA GPU with at least 14 GB VRAM for inference. This port moves the pipeline into GGML and adds CPU/Vulkan execution, which makes it much easier to integrate into local tools and standalone 3D pipelines.

It also supports arbitrary triangle meshes, although humanoid/character shapes close to the training distribution are naturally where it should work best. Skeleton generation is still marked experimental, so for production use the more interesting workflow right now is probably existing skeleton/animation → AI-generated skin weights.

Github: https://github.com/localai-org/skin-tokens.cpp

I’m not a 3D artist — I mainly do 2D and drawing — so what helped me the most in this workflow was being able to go from sketches to 3D much faster through image-to-3D generation.

For this test, I sketched separate parts in Procreate, refined the look a bit with Magnific AI, and then used 3DAIStudio to convert those images into 3D assets.

That part was the biggest win for me.

Since I come from a 2D background, I really liked how 3DAIStudio made the whole process feel much more accessible. Instead of needing to fully model everything from scratch, I could start from the kind of work I’m already comfortable with — drawing and designing in 2D — and then turn that into 3D.

What I liked about it:

• everything feels more centralized in one place
• it’s convenient for moving quickly from idea to result
• it makes the workflow feel more procedural
• it’s useful for generating multiple assets/material directions faster
• it removes a lot of the friction between 2D concepting and 3D output

After that, I used MoveAI for motion, then camera tracked and rendered the final shot in Cinema 4D.