Z

🎮 AI-generated 3D models often have hundreds of thousands of unnecessary polygons. A new ComfyUI node can rebuild them into clean low-poly meshes with just a few thousand. Meet ComfyUI-LATO2, a new open-source node that brings the LATO.2 neural remeshing model directly into ComfyUI. It works with dense meshes generated by TRELLIS.2, Hunyuan3D and other 3D tools. And unlike traditional polygon reduction, it doesn't simply delete triangles. It rebuilds the geometry from scratch. Why does this matter? AI 3D generators frequently produce meshes containing 500K–1M triangles. They can look impressive, but using them directly in games or real-time applications creates problems: ▪️ Excessive geometry and rendering overhead. ▪️ Poorly distributed polygons. ▪️ Unnecessary internal surfaces. ▪️ Topology that doesn't follow the object's shape. ▪️ Difficult manual cleanup. ComfyUI-LATO2 addresses these issues using a neural topology-generation model. How does LATO.2 work? The original model was developed by researchers from Huazhong University of Science and Technology, Meshy AI and Technical University of Munich. It separates mesh generation into two stages: 1. V-Flow: Vertex Generation A learned flow model creates new vertex positions based on the input object's shape and a target vertex count. You can request anywhere from 200 to 5,000 vertices. 2. T-Flow: Topology Generation A second model predicts which vertices should connect, producing edges that better follow the object's geometry. This is fundamentally different from a traditional decimator that simplifies an existing triangle structure. But the ComfyUI integration goes further. The original LATO.2 can produce overlapping faces, inconsistent winding and holes. Developer sakalond added several geometry-processing steps: ▪️ Surface reconstruction: Rebuilds faces from predicted connections, preventing edges from belonging to more than two faces. ▪️ Hole filling: Uses minimum-area triangulation to close gaps. ▪️ Outward orientation: Determines which direction each face should point. ▪️ Internal surface removal: Uses voxel flood fill to eliminate hidden inner walls frequently found in TRELLIS.2 meshes. ▪️ Transform preservation: Restores the original position and scale, allowing the new mesh to replace the source object. The result? You can generate a dense 3D object, process it through LATO.2 and export a much lighter GLB without manually rebuilding its topology. The node integrates with ComfyUI's native MESH type, Preview 3D and Save 3D Model. Four example workflows are included. Hardware requirements: ▪️ NVIDIA GPU with at least 8GB VRAM. ▪️ Windows or Linux. ▪️ Approximately 13GB of disk space. ▪️ Python and CUDA-compatible PyTorch. The installer automatically creates an isolated environment and downloads the required checkpoints. No manual compilation of CUDA extensions is needed. Performance: On RTX 3090/4080 GPUs: ▪️ 30–60 seconds for neural remeshing. ▪️ An additional 1–3 minutes may be needed to remove internal geometry from extremely dense meshes. Important limitations: ▪️ Geometry only. No UVs, textures or materials are transferred. ▪️ Thin objects can produce folds or self-intersections. ▪️ Narrow openings may get unintentionally closed. ▪️ Different seeds can generate different topology. ▪️ The resulting geometry isn't guaranteed to be suitable for 3D printing or Boolean operations. The node is released under GPL-3.0. Why this matters: AI can already generate impressive 3D objects from images. But generating geometry and producing optimized assets for a real game engine are two different problems. ComfyUI-LATO2 helps bridge that gap. Imagine a workflow where AI creates a model, another model rebuilds its topology, and the result moves into Blender, Godot or Unreal Engine for further processing. AI-generated 3D is moving beyond making objects look good toward making them practical to use. 💻 github.com/sakalond/ComfyUI-… 🔬 Original LATO.2 research: lohhhha.github.io/LATO.2/ #ComfyUI #3DGeneration #GameDev #OpenSource