| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-3d |
| base_model: Lucasgitguy-co/image3d-student |
| model_name: Forma3D-Tiny v0.2 |
| datasets: |
| - allenai/objaverse |
| - scenario-labs/3d-collection |
| tags: |
| - pytorch |
| - gaussian-splatting |
| - research-prototype |
| - mesh-proxy-supervision |
| --- |
| # Forma3D-Tiny v0.2 |
|
|
| An experimental compact image-to-Gaussian model post-trained from Image3D Student's |
| synthetic demonstration checkpoint. This is a coarse research baseline, not a |
| general-purpose photo-to-3D model. Its grid is 16 cubed and |
| it has 306,321 parameters. Selected checkpoint: step 4800; |
| completed run: 5000 steps. |
|
|
| ## Actual training data and preprocessing |
|
|
| Only the datasets listed above were used, with per-asset provenance in ATTRIBUTIONS.json. |
| Scenario assets are CC-BY-4.0; the Objaverse subset is CC0. Original objects were centered |
| and uniformly scaled into a 0.9-wide cube, surface sampled, and converted to isotropic |
| Gaussian proxy targets. Orthographic images were rendered from these same proxy clouds. |
| This is not teacher distillation from Idol, Hunyuan, InstantMesh, or Hi3D, and it did not |
| train on natural photographs, MultiScan, 3D-Front, Show3D, or Voxel51 scene splats. |
|
|
| Related source inputs and all views stay in the same object group. A separate 15% test |
| partition is excluded from training and validation-based checkpoint selection. |
|
|
| ## Measured results |
|
|
| Independent test objects: 14; test views: 60. |
| Base test loss: 0.842823; post-trained test loss: |
| 0.584055. Base occupancy IoU: |
| 0.272567; post-trained occupancy IoU: |
| 0.307005. |
| See evaluation.json for the full results and checks. These scores measure coarse proxy |
| reconstruction on the rendered data distribution; they do not prove photo realism, |
| watertight meshes, production quality, or texture fidelity. Near duplicates can remain |
| despite source grouping and exact-byte deduplication. |
|
|
| ## Run on a Mac |
|
|
| Download this repository, then run `bash setup-mac.sh` and |
| `source "$HOME/Library/Application Support/Forma3D/venv/bin/activate"`. |
|
|
| ```sh |
| image3d generate --checkpoint model.pt --image your-centered-cutout.png \ |
| --out results/object.ply --device mps |
| image3d preview --ply results/object.ply --out results/preview.png |
| ``` |
|
|
| Inputs are centered objects on black, matching the training framing. Natural photos |
| are outside the verified distribution. Output is Gaussian PLY, not a triangle mesh. |
| CPU works with `--device cpu`; CUDA support is implemented but was not tested on the |
| Mac used to prepare these scripts. |
|
|
| ## License and attribution |
|
|
| Source code and these weights are offered under Apache-2.0. Dataset and individual |
| asset licenses remain separate; retain ATTRIBUTIONS.json and consult DATASETS.md. |
| This license statement does not grant rights to third-party trademarks or likenesses. |
| See COMMANDS.md for reproduction commands. |
|
|
