Simplify Arbor model card

README.md

@@ -14,9 +14,7 @@ Explicit Geometric Conditioning for Controllable 3D Asset Generation
 
 ![Arbor teaser](./assets/arbor_teaser.jpg)
 
-[Project Page](https://arbor.jdihlmann.com/) | [Paper](https://arbor.jdihlmann.com/downloads/arbor_paper.pdf) | [GitHub](https://github.com/Stability-AI/arbor) | [Citation](#citation)
-
-Arbor generates 3D assets from a text prompt and typed geometry constraints. Hull regions mark where geometry should exist, avoidance regions mark space that should stay empty, and touch regions mark surfaces the object should contact or keep reachable.
+Arbor is a text-to-3D generation model with explicit geometry controls. It accepts a prompt and typed constraint meshes for hull, avoidance, and touch regions.
 
 Please note: For individuals or organizations generating annual revenue of US $1,000,000 (or local currency equivalent) or more, regardless of the source of that revenue, you must obtain an enterprise commercial license directly from Stability AI before commercially using Arbor, derivative works of Arbor, or outputs from Arbor. See https://stability.ai/license and https://stability.ai/enterprise.
 
@@ -25,9 +23,16 @@ Please note: For individuals or organizations generating annual revenue of US $1
 - Developed by: [Stability AI](https://stability.ai/)
 - Authors: Jan-Niklas Dihlmann, Andreas Engelhardt, Simon Donne, Hendrik Lensch, Mark Boss
 - Model type: Text and geometry constrained 3D generation model
+- Model details: Arbor generates a geometry-conditioned sparse structure that is decoded into a mesh with the TRELLIS SLAT mesh decoder.
+- Base stack: TRELLIS text-to-3D and TRELLIS.2 constraint encoding components
+
+## Model Sources
+
 - Repository: https://github.com/Stability-AI/arbor
 - Project page: https://arbor.jdihlmann.com/
-- Base stack: TRELLIS text-to-3D and TRELLIS.2 constraint encoding components
+- Paper: https://arbor.jdihlmann.com/downloads/arbor_paper.pdf
+
+For installation, inference examples, evaluation, Blender notes, and demo assets, please use the [GitHub repository](https://github.com/Stability-AI/arbor).
 
 ## Files
 
@@ -36,71 +41,12 @@ Please note: For individuals or organizations generating annual revenue of US $1
 - `denoiser_ema0.9999_step0053000.pt`: public Arbor sparse-structure checkpoint.
 - Optional parity artifact, if uploaded: `denoiser_step0053000.pt`.
 
-The GitHub code downloads these files automatically when possible. You can also download them explicitly with the helper script shown below.
-
-## Quickstart
-
-```bash
-git clone https://github.com/Stability-AI/arbor.git
-cd arbor
-
-conda create -n arbor python=3.10 -y
-conda activate arbor
-conda install pytorch==2.4.0 torchvision==0.19.0 pytorch-cuda=11.8 -c pytorch -c nvidia -y
-./setup.sh --basic --xformers --spconv
-
-hf auth login
-hf auth whoami
-python scripts/download_model_from_hf.py --repo-id StabilityLabs/arbor --output-dir artifacts/model
-python examples/run_manual_example.py --example chair_080 --output-root outputs/manual_examples --evaluate
-```
-
-For non-interactive shells, pass a token without saving it in shell history:
-
-```bash
-read -s HF_TOKEN
-hf auth login --token "$HF_TOKEN"
-unset HF_TOKEN
-hf auth whoami
-```
-
-## Demo Examples
-
-The GitHub release includes curated constraint meshes and a public evaluator that renders condition-following panels.
-
-![Arbor demo examples](./assets/demo_examples.png)
-
-Run the examples with:
-
-```bash
-python examples/run_manual_example.py --example bus_013 --output-root outputs/manual_examples --evaluate
-python examples/run_manual_example.py --example chair_080 --output-root outputs/manual_examples --evaluate
-python examples/run_manual_example.py --example piano_038 --output-root outputs/manual_examples --evaluate
-python examples/run_manual_example.py --example sink_015 --output-root outputs/manual_examples --evaluate
-```
-
-## Evaluation Outputs
-
-The public `arbor-evaluate` command computes condition metrics and renders inspection panels without training code or benchmark launchers.
-
-```bash
-arbor-evaluate \
-  --prediction outputs/manual_examples/bus_013/mesh.ply \
-  --hull examples/constraints/bus__bus_013__19cbb410/hull_constraint.ply \
-  --avoidance examples/constraints/bus__bus_013__19cbb410/avoidance_constraint.ply \
-  --touch examples/constraints/bus__bus_013__19cbb410/touchable_constraint.ply \
-  --reference-mesh examples/constraints/bus__bus_013__19cbb410/gt_mesh.ply \
-  --sparse-structure outputs/manual_examples/bus_013/ss_surface.npz \
-  --output-dir outputs/manual_examples/bus_013/evaluation
-```
-
-`--reference-mesh` is optional. It adds reference metrics such as Chamfer distance and F-score. It is not used for generation.
-
-The evaluator writes `metrics.json`, `metrics_flat.json`, and a four-view `condition_panel.png`. Pass `--save-gif` to also write `condition_orbit.gif`; the default speed is 150 ms per frame.
+## License
 
-## Coordinate Note
+- Community License: Free for research, non-commercial, and commercial use by organizations and individuals generating annual revenue of US $1,000,000 (or local currency equivalent) or less, regardless of the source of that revenue.
+- If your annual revenue exceeds US $1M, any commercial use of this model or derivative works requires an Enterprise License directly from Stability AI.
 
-Arbor follows constraints inside the normalized TRELLIS object box. It does not generate in metric world units. TRELLIS tends to center the asset and make it use the available box, so constraints should describe relative shape inside that box. If one region grows, another part can look smaller because the final asset is still normalized to the same space.
+Please refer to Stability AI's Community License at https://stability.ai/license for more information.
 
 ## Intended Uses