| --- |
| license: apache-2.0 |
| pipeline_tag: image-to-3d |
| base_model: Lucasgitguy-co/forma3d-tiny-2 |
| base_model_relation: finetune |
| model_name: Forma3D Tiny 3 |
| datasets: |
| - codepawl/abo-glb |
| tags: |
| - pytorch |
| - gaussian-splatting |
| - mesh-proxy-supervision |
| - research-prototype |
| --- |
| # Forma3D Tiny 3 |
|
|
| Continued from [Lucasgitguy-co/forma3d-tiny-2](https://huggingface.co/Lucasgitguy-co/forma3d-tiny-2) at `1648582b07a0f7f9d482a8c0f4cf89e6ecd86972`. |
| Same 306,321-parameter architecture, 16 cubed grid, width 64, maximum 4,096 Gaussian slots. |
| Fresh AdamW optimizer at the start of continuation. Completed 6000 steps; |
| selected step 6000 by validation loss only. |
|
|
| ## Data and scope |
|
|
| 512 ABO objects, 2048 orthographic Gaussian-proxy images on black. |
| Data revision: `c7c8293fed3b7ab122ef5d7896521a187e0962d7`. Targets are isotropic mesh surface samples, compressed to |
| one Gaussian per occupied grid cell. Texture surface colors are sampled; full PBR rendering |
| and differentiable rendering are not used. No natural-photo training is claimed. |
| MeshAnythingV2, Idol Diffusers, SHARP, Pixal3D, Idol Turbo and Qwen3.5-4B-3D-Coder |
| were **not run, merged, or distilled** in this Mac-only recipe. |
|
|
| Objects, shared Amazon item IDs and exact mesh/image/target hashes are grouped before splitting. |
| Split groups: {"train": 348, "validation": 87, "test": 77}. Test data is excluded from training and checkpoint selection. |
| Near duplicates can remain; overlap with ancestor training datasets is not fully known. |
|
|
| ## Evaluation |
|
|
| Both checkpoints were evaluated on the identical held-out ABO proxy views. |
|
|
| | Metric | Starting v2 | Selected v3 | |
| |---|---:|---:| |
| | Mean test loss | 0.690551 | 0.544487 | |
| | Mean view occupancy IoU, threshold 0.5 | 0.286866 | 0.339860 | |
|
|
| These measurements do not establish natural-photo reconstruction, photorealism, mesh quality, |
| novel-view rendering quality or improvement on v2's original domains. No confidence intervals |
| or broad generalization claim. See evaluation.json, preparation.json and split manifests. |
|
|
| ## Use |
|
|
| With Python 3.11, install this repository using `pip install .`, then: |
|
|
| ```bash |
| image3d generate --checkpoint model.pt --image centered-object-on-black.png --out object.ply --device mps |
| image3d preview --ply object.ply --out preview.png |
| ``` |
|
|
| Output is Gaussian PLY, not a textured GLB mesh. Inputs should match centered objects on black. |
| The model has no text prompt conditioning. Reproduction launcher and locked dependencies are in recipe/. |
|
|
| ## Attribution |
|
|
| Publisher source and weights: Apache 2.0. Retain inherited NOTICE, THIRD_PARTY.md and ATTRIBUTIONS.json. |
| ABO models, images and listing text: Amazon.com and the Amazon Berkeley Objects contributors, |
| CC BY 4.0; converted to GLB by CodePawl. The recipe further decompresses geometry, |
| normalizes coordinates, samples colored surfaces and renders proxy images. |
| See [ABO dataset](https://huggingface.co/datasets/codepawl/abo-glb), |
| ATTRIBUTIONS.json and licenses/LICENSE-ABO-CC-BY-4.0.txt. Raw assets are not bundled. |
| |
