InstantMesh vs PartCrafter
InstantMesh
github.comPartCrafter
github.comSide by side
| Figure | InstantMesh | PartCrafter |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | — | — |
| Windows | — | Yes |
| Linux | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 1 | 1 |
| Spec score · AI 3D Model Generators | 7.7 (#17) | 8.3 (#8) |
| Deployment | The README documents running the demo locally or with Docker. | The official instructions install the project from its GitHub repository in a Python environment and run inference scripts locally. |
| License | The repository includes an Apache License 2.0. | The repository lists an MIT license. |
| Purpose | InstantMesh is a feed-forward framework for generating a 3D mesh from a single image using the LRM/Instant3D architecture. | PartCrafter generates multiple structured 3D parts and objects from a single RGB image in one pass. |
Plans
In their own words
InstantMesh
InstantMesh is a free, Apache-licensed framework that generates a 3D mesh from one image using the LRM/Instant3D architecture. It runs on Linux in a local or self-hosted setup. A Gradio demo starts with `python app.py`, while `run.py` offers command-line generation. The default export is an OBJ mesh with vertex colors; an alternative adds a texture map and takes longer. rembg can segment the foreground object, or that step can be skipped when the source already has an alpha mask. Four sparse-view reconstruction model variants and a customized Zero123++ UNet for white-background image generation are included. Docker deployment is documented. The recommended environment uses Python 3.10 or later, PyTorch 2.1.0 or later and CUDA 12.1 or later; two GPUs can reduce demo memory use. Training code is included, but the training dataset is unavailable because of its size. Community implementations are noted for ComfyUI, Replicate and Google Colab.
PartCrafter
PartCrafter is a free, open-source model that generates multiple 3D parts and objects from a single RGB image. It can handle individual objects and complex scenes, producing distinct meshes with meaningful part structure without requiring a pre-segmented image. Its architecture represents parts with disentangled latent tokens and uses hierarchical attention to pass information within and across parts. Local inference scripts can remove backgrounds and optionally apply style transfer to adapt real-world photos toward the rendered-image domain used for training. An optional vision-language feature can suggest the number of parts; Gemini is the default provider, and the provider architecture can be extended. Pretrained object-level and scene-level weights are hosted on Hugging Face. Installation is from the GitHub repository in a Python environment, and inference requires a CUDA-enabled GPU with at least 8 GB of VRAM. The linked Hugging Face demo is paused, though its page says users can ask the authors to restart it. The repository lists an MIT license and provides the software as-is, without warranty.