| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-3d |
| base_model: Lucasgitguy-co/forma3d-tiny |
| base_model_relation: finetune |
| model_name: Forma3D Tiny 2 |
| datasets: |
| - allenai/objaverse |
| - scenario-labs/3d-collection |
| tags: |
| - pytorch |
| - gaussian-splatting |
| - mesh-proxy-supervision |
| - research-prototype |
| --- |
| # Forma3D Tiny 2 |
|
|
| A 306,321-parameter image-to-Gaussian research model continued from |
| Lucasgitguy-co/forma3d-tiny at revision `e80b8d895cc2e405894473abe4ce40b3472eaca7`. The optimizer starts fresh. It uses a |
| 16 cubed grid and width 64. |
| Selected checkpoint step 7800 from 8000 completed steps. |
|
|
| ## Training |
|
|
| See training.json and metrics.jsonl for actual optimizer settings and validation |
| history. Inputs are orthographic renders of centered mesh-derived Gaussian proxy |
| clouds on black. Targets are isotropic surface samples compressed to one Gaussian |
| per occupied grid cell. No pretrained 3D teacher was run. No natural-photo training |
| or differentiable rendering is claimed. Asset IDs, known source groups, and exact |
| mesh hashes from all 96 known v1 assets were excluded. Near duplicates can remain. |
| The separate 15 percent object-group test split does not enter checkpoint selection; |
| 20 percent of development groups supply validation. Counts are realized counts in |
| preparation.json and data-audit.json, not assumed independent asset counts. |
|
|
| ## Evaluation |
|
|
| Both checkpoints were evaluated on the same new held-out proxy data. |
|
|
| | Metric | Starting Forma3D Tiny | Selected Forma3D Tiny 2 | |
| |---|---:|---:| |
| | Mean test loss | 0.616513 | 0.570490 | |
| | Mean view occupancy IoU at threshold 0.5 | 0.286327 | 0.327870 | |
|
|
| Test groups: 36; views: 156. |
| See evaluation.json for hashes and release gates. Metrics are view-averaged over |
| proxy renders. They do not establish photo realism, novel-view image quality, |
| mesh quality, texture fidelity, gameplay, or general-purpose 3D reconstruction. |
|
|
| ## Use |
|
|
| Install this source with Python 3.11 and `uv pip install ".[data]"` in a virtual |
| environment, then run: |
|
|
| ```bash |
| 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 |
| ``` |
|
|
| Output is Gaussian PLY. Inputs should match centered objects on black. See |
| COMMANDS.md for the complete continuation recipe. This model retains the same |
| architecture; no text-prompt conditioning or new mesh decoder was added. |
|
|
| ## License |
|
|
| Publisher source and weights are offered under Apache 2.0. Retain NOTICE, |
| THIRD_PARTY.md and ATTRIBUTIONS.json. CC BY Scenario assets and CC0 Objaverse |
| assets have their own attribution and license records in DATASETS.md. |
| |
