TL;DR: runs locally on an NVIDIA GPU with 32 GB or more. Repo with an example: https://github.com/MCPBytes/mcpbytes-image-to-3d uvx --python 3.12 --torch-backend=auto mcpbytes-image-to-3d chair.jpg The first run downloads about 19 GB of model files and compiles two small CUDA extensions. Results go to ./chair-3d/. I'm the developer. It's a harness around Tencent's Hunyuan3D 2.1 (whose code it vendors), not a new model. You give it one image of an object and get back a model you can use right away: model.glb: one mesh with base-colour and metallic-roughness textures at 1024 px, +Y up, standing on the ground, largest side 1 unit model.web.glb: the same model with Draco geometry and WebP textures, for browsers and engines model_preview.png: the finished model from six sides on one sheet, so you can check the back and underside without opening a viewer How it works: The object is cut out (a transparent PNG is used as-is; otherwise U²-Net removes the background) and centred on a square canvas. Hunyuan3D 2.1's shape model builds the mesh (50 steps, octree 384), it's remeshed to 120,000 faces, and the paint model textures it from 8 views at 768 px (6 views at 512 px and 40,000 faces on cards under 48 GB). Then plain CPU cleanup: weld, drop small floating debris, centre, ground, scale to 1, recompute normals, write the web copy and render the six views. The same seed, image, settings and GPU gave me the same files byte for byte. It also runs as an MCP server, so Claude, Cursor and other MCP clients can make models themselves. What it won't do: the back and underside are invented from one picture, and nothing about physical size, rigging, watertightness or print readiness is inferred. Look at the preview before you use a model. Hardware: NVIDIA with 32 GB or more on Linux or WSL2, plus the CUDA 12 toolkit and a C++ compiler for the first-run build. On an A40 a model took 386 s at full detail (41 GB peak) and 203 s at reduced detail (28 GB), including about two minutes of loading. My code is AGPL-3.0; the vendored Hunyuan3D code and the weights are under Tencent's community licence, which doesn't cover the EU, the UK or South Korea. What you generate is yours. Repo: https://github.com/MCPBytes/mcpbytes-image-to-3d If you try it, tell me where it breaks. Odd objects welcome. If you don't have a GPU powerful enough. Try the cloud option or https://mcpbytes.com/tools/text-to-game-asset (yes cost money hence my contribution to open source for all of you without enough resources)   submitted by   /u/JuanixVentures   to   r/aigamedev [link]   [comments]