Pretrained BRDF decoders, ready-to-render neural cloth materials, and the scenes and media from the RoboCloth paper. Every stage-2 checkpoint here is a complete material — a dense latent texture plus its frozen decoder — so you can render it without downloading any capture data.

Capture dataset: koalapenguin/RoboCloth
Code: https://github.com/colinzhenli/robocloth (Apache-2.0)
Project page: https://colinzhenli.github.io/BRDF-Fipt/

Layout

checkpoints/
  stage1/{Ours,Bonn,MERL}.ckpt        # shared BRDF decoders           ~4.2 GB
  stage2/RoboCloth/<13 materials>/    # RoboCloth test + teaser mats    ~31 GB
  stage2/Bonn/<5 materials>/          # held-out UBOFAB19 materials     ~2.1 GB
  stage2/UBO/<12 materials>/          # held-out UBO2014 materials      ~2.5 GB
render_assets/
  cloth_on_bar/                       # onBars01_st_hp.ply, pole_spheres_v4.obj   ~22 MB
  teaser_room/                        # room.xml, meshes/ (35 .ply), textures/ (1)   ~125 MB
renders/
  cloth_on_bar/cloth_on_bar_2048_spp512.{png,exr}   # reference render, 2048², 512 spp
  teaser/teaser_1080p_spp8.{png,exr}                # draft teaser render, 1920×1080, 8 spp
media/
  teaser.png                          # paper Fig. 1
  capture_314_5x.mp4                  # one material being captured, 5x speed
  web/cloth_with_sphere_final.mp4     # project-page video

Total ≈ 40 GB; the stage-2 RoboCloth checkpoints dominate.

Checkpoint naming

checkpoints/<stage>/<test set>/<material>/<decoder source>_epoch<N>.ckpt

Stage 1 — the shared MLP BRDF prior, one per training corpus: Ours.ckpt (trained on RoboCloth, 2.0 GB), Bonn.ckpt (UBOFAB19, 2.2 GB), MERL.ckpt (MERL measured BRDFs, 1.5 MB — no per-point latent bank, hence the size). Stage 2 loads only the material.decoder.* tensors from these.
Stage 2 — one fitted material. The prefix names the frozen stage-1 decoder the fit started from: Ours (RoboCloth prior), Bonn, MERL, or PBR for the analytic Disney baseline fitted in place of a neural decoder.
epoch<N> is the checkpoint's stored epoch plus one, i.e. epoch80 was written after 80 completed epochs (stored epoch index 79). It is part of the filename, not a knob — match it with a glob rather than typing it.

Each stage-2 file holds material.latent_texture.params (1,C,R,R) (C = 24 latent + 16 geometry + 6 frame), a frozen copy of material.decoder.*, the per-channel scale material.factor (3,), and the parallax query material.neural_geometry.*.

What is released

Set	Materials	Variants per material
`stage2/RoboCloth`	5 paper test materials — 145, 226, 314, 370, 452	`Ours`, `Bonn`, `MERL`, `PBR`
`stage2/RoboCloth`	8 teaser materials — 8, 9, 26, 50, 190, 311, 367, 453	`Ours` only
`stage2/Bonn`	32, 37, 226, 318, 377	`Ours`, `Bonn`, `MERL`, `PBR`
`stage2/UBO`	carpet02, carpet07, carpet09, carpet12, fabric02, fabric04, fabric09, fabric11, felt01, felt03, felt05, felt10	`Ours`, `Bonn`, `MERL`, `PBR`

Material ids under stage2/RoboCloth/ are the same ids as in the capture dataset; the Bonn and UBO sets are the held-out materials of the two external benchmarks used in the paper.

Third-party datasets are not redistributed here. The MERL BRDF database and the UBO2014 BTFs have their own licenses and must be obtained from their owners; the code repository documents how to point the comparison scripts at your own copies. Only checkpoints trained on them are released, under this repository's license.

Download

The hf command ships with huggingface_hub (pip install -U "huggingface_hub[cli]"). Quote every --include pattern — an unquoted * is expanded by the shell before it reaches the include filter.

The five paper materials plus the eight teaser materials (~31 GB):

hf download koalapenguin/RoboCloth-assets --repo-type dataset \
    --include "checkpoints/stage2/RoboCloth/*" --local-dir ./robocloth-assets

One material only (~1–3 GB):

hf download koalapenguin/RoboCloth-assets --repo-type dataset \
    --include "checkpoints/stage2/RoboCloth/145/*" --local-dir ./robocloth-assets

The pretrained decoder, if you want to fit your own materials (~2 GB):

hf download koalapenguin/RoboCloth-assets --repo-type dataset \
    --include "checkpoints/stage1/Ours.ckpt" --local-dir ./robocloth-assets

The bundled example scenes:

hf download koalapenguin/RoboCloth-assets --repo-type dataset \
    --include "render_assets/*" --local-dir ./robocloth-assets

Using the checkpoints

Set up the rendering environment and run rendering/render.py from the code repository — its README walks through the whole path. A scene is a folder with a Mitsuba 3 scene.xml, its meshes, and a materials.json that assigns checkpoints to shape ids:

{
  "checkpoint_root": "/absolute/path/to/robocloth-assets/checkpoints/stage2/RoboCloth",
  "assignments": {
    "sofa":    {"material": "370"},
    "curtain": {"material": "145", "uv_tiling": 12.0}
  }
}

"material": "<id>" resolves to <checkpoint_root>/<id>/Ours_epoch*.ckpt (the glob must match exactly one file), so you never hard-code the epoch; "ckpt" takes an explicit path instead. Shapes not listed keep the BSDF from the XML. Meshes need UVs — the material is a latent texture — and uv_tiling sets the weave repeat density. The repository's examples/cloth_on_bar and examples/teaser scenes consume render_assets/cloth_on_bar/ and render_assets/teaser_room/, relocated through the MESH_DIR, TEASER_SCENE_ROOT and BRDF_CKPT_ROOT environment variables. The stage-1 decoder is the starting point for fitting a new material (scripts/train_stage2.sh).

Renders and media

renders/cloth_on_bar/ is the reference output of the bundled cloth-on-bar example at 2048×2048 and 512 spp, as a tone-mapped PNG and the linear EXR — useful for checking that your setup reproduces the released result; renders/teaser/ is a draft-quality (8 spp) render of the bundled teaser scene for the same purpose. media/teaser.png is paper Fig. 1, media/capture_314_5x.mp4 shows one material being captured at 5× speed, and media/web/ holds the project-page media.

Provenance of the teaser room scene

render_assets/teaser_room/ is a third-party interior scene built from freely available assets — the meshes and the textures/ image were not created by the RoboCloth authors. It is included so the paper's teaser figure can be reproduced; only the cloth surfaces in it are RoboCloth materials. Those assets keep their original terms and are not covered by this repository's CC BY 4.0 grant; check them before redistributing the folder on its own.

Citation

@misc{robocloth2026,
  title  = {RoboCloth: A Large-Scale Real Cloth Material Dataset for Neural Reflectance Reconstruction},
  author = {Anonymous},
  year   = {2026},
  note   = {Under review},
  url    = {https://huggingface.co/datasets/koalapenguin/RoboCloth}
}

License and contact

CC BY 4.0, except the third-party scene assets noted above (free assets, original terms apply). The code repository is Apache-2.0. The paper is under review, so the authors are anonymous for now — please open a discussion here or an issue on the code repository.

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