Point the card, croissant, example, sample README and Data Studio index at theialab/RoboCloth

README.md

@@ -30,7 +30,7 @@ configs:
 
 # RoboCloth
 
-![Left: the robotic capture rig with one captured frame inset. Right: held-out RoboCloth materials path-traced as the sofa, curtain, pillow and carpet fabrics of a room scene.](https://huggingface.co/datasets/koalapenguin/RoboCloth-assets/resolve/main/media/teaser.png)
+![Left: the robotic capture rig with one captured frame inset. Right: held-out RoboCloth materials path-traced as the sofa, curtain, pillow and carpet fabrics of a room scene.](https://huggingface.co/datasets/theialab/RoboCloth-assets/resolve/main/media/teaser.png)
 
 **500 real cloth materials, each imaged in ≈580 calibrated 16-bit linear HDR frames by a
 robotic camera-and-light rig.** Two robot arms move the camera and a single LED over a
@@ -45,7 +45,7 @@ small MLP.
 
 - **Project page:** <https://theialab.github.io/robocloth/>
 - **Code:** <https://github.com/theialab/robocloth> (Apache-2.0)
-- **Checkpoints and render assets:** [`koalapenguin/RoboCloth-assets`](https://huggingface.co/datasets/koalapenguin/RoboCloth-assets)
+- **Checkpoints and render assets:** [`theialab/RoboCloth-assets`](https://huggingface.co/datasets/theialab/RoboCloth-assets)
   — pretrained decoders and self-contained, renderable per-material checkpoints (no capture
   data needed to render them)
 
@@ -53,7 +53,7 @@ small MLP.
 
 ```bash
 pip install -U huggingface_hub numpy pillow
-hf download koalapenguin/RoboCloth --repo-type dataset \
+hf download theialab/RoboCloth --repo-type dataset \
     --include "globals/*" --include "examples/*" --include "materials/145/*" --local-dir ./RoboCloth   # one material, 3-10 GB
 python RoboCloth/examples/load_material.py --mid 145   # prints every array, extracts one frame from hdr.tar
                                                       # (fetches through the Hub cache, not from ./RoboCloth)
@@ -61,7 +61,7 @@ python RoboCloth/examples/load_material.py --mid 145   # prints every array, ext
 
 ```python
 import numpy as np; from huggingface_hub import hf_hub_download
-obs = np.load(hf_hub_download("koalapenguin/RoboCloth", "materials/145/observations_structured.npz", repo_type="dataset"))
+obs = np.load(hf_hub_download("theialab/RoboCloth", "materials/145/observations_structured.npz", repo_type="dataset"))
 rgbs, xyz = obs["rgbs"], obs["xyz"]                     # (K,V,3) uint16, 0 = unobserved · (V,3) float32 m
 cam_pos, light_pos = obs["cam_pos"], obs["light_pos"]   # (K,3) float32 mm: robot-logged gripper / LED positions
                                                         # (= scan_log.json), not the SfM camera centres
@@ -72,7 +72,7 @@ Quote every `--include` pattern. A 0.5 GB [sample subset](#whats-in-a-material-f
 more download recipes are under Details below.
 
 
-**Browse before downloading:** the *Data Studio* tab shows `index/robocloth_index.parquet`, one row per material: `thumbnail` (one frontal frame, rectified to the sample square), `split`, `width_cm` / `height_cm` (sample size), `frames` (HDR frames in `hdr.tar`), `lights` (distinct light positions), `observations` / `points` (rows and surface points of the observation tensor), `thumb_mean_srgb` (mean colour of the thumbnail — not an albedo), `hdr_frames_gb` (size of `hdr.tar`), `observation_tensor_gb` (size of `observations_structured.npz`), and links to the interactive viewer and the material's folder. To fetch a material you found there: `hf download koalapenguin/RoboCloth --repo-type dataset --include "globals/*" --include "materials/<id>/*" --local-dir ./RoboCloth`.
+**Browse before downloading:** the *Data Studio* tab shows `index/robocloth_index.parquet`, one row per material: `thumbnail` (one frontal frame, rectified to the sample square), `split`, `width_cm` / `height_cm` (sample size), `frames` (HDR frames in `hdr.tar`), `lights` (distinct light positions), `observations` / `points` (rows and surface points of the observation tensor), `thumb_mean_srgb` (mean colour of the thumbnail — not an albedo), `hdr_frames_gb` (size of `hdr.tar`), `observation_tensor_gb` (size of `observations_structured.npz`), and links to the interactive viewer and the material's folder. To fetch a material you found there: `hf download theialab/RoboCloth --repo-type dataset --include "globals/*" --include "materials/<id>/*" --local-dir ./RoboCloth`.
 
 ## What's in a material folder
 
@@ -197,7 +197,7 @@ the include filter.
 Sample subset plus all calibration and split files (≈0.5 GB):
 
 ```bash
-hf download koalapenguin/RoboCloth --repo-type dataset \
+hf download theialab/RoboCloth --repo-type dataset \
     --include "sample/*" --include "globals/*" --include "examples/*" \
     --local-dir ./robocloth-sample
 ```
@@ -205,7 +205,7 @@ hf download koalapenguin/RoboCloth --repo-type dataset \
 Sparse only (≈490 GB) — everything stage-1 decoder training needs, without the HDR frames:
 
 ```bash
-hf download koalapenguin/RoboCloth --repo-type dataset \
+hf download theialab/RoboCloth --repo-type dataset \
     --include "globals/*" \
     --include "materials/*/observations_structured.npz" \
     --include "materials/*/scan_log.json" \
@@ -217,7 +217,7 @@ hf download koalapenguin/RoboCloth --repo-type dataset \
 Everything (3.39 TB):
 
 ```bash
-hf download koalapenguin/RoboCloth --repo-type dataset --local-dir ./RoboCloth
+hf download theialab/RoboCloth --repo-type dataset --local-dir ./RoboCloth
 ```
 
 The code repository wraps these in `scripts/download_material.sh <id>`,

croissant.jsonld

@@ -220,7 +220,7 @@
       "@id": "globals-camera-factor-json",
       "name": "globals-camera-factor-json",
       "description": "Per-material exposure-regime index (camera_factor_segments: id ranges -> factor 1, 2 or 3).",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/camera_factor.json",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/camera_factor.json",
       "encodingFormat": "application/json",
       "contentSize": "3923 B",
       "sha256": "442a5494166ae9b61f428920f6b4ec4178e5aa671f521a674c37fa8a12a5451a"
@@ -230,7 +230,7 @@
       "@id": "globals-emitter-calibration-json",
       "name": "globals-emitter-calibration-json",
       "description": "Light emitter intensity profile by angular position (degrees).",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/emitter_calibration.json",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/emitter_calibration.json",
       "encodingFormat": "application/json",
       "contentSize": "3027 B",
       "sha256": "78e263ce2c7095c1abd9cf4ab194407141974912a56c4199fe40ca71de1a8d41"
@@ -240,7 +240,7 @@
       "@id": "globals-sample-size-json",
       "name": "globals-sample-size-json",
       "description": "Physical sample width and length (m) per material-id range, used to crop the point cloud.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/sample_size.json",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/sample_size.json",
       "encodingFormat": "application/json",
       "contentSize": "7142 B",
       "sha256": "190f83e9a86da3ead2f435017d0506898ae001d5b9fa66fee2d24711ef54a81f"
@@ -250,7 +250,7 @@
       "@id": "globals-training-list-100-txt",
       "name": "globals-training-list-100-txt",
       "description": "Training material ids for the 100-material subset.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/training_list_100.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/training_list_100.txt",
       "encodingFormat": "text/plain",
       "contentSize": "299 B",
       "sha256": "7cf26c4900a3760ad1489a2e890f81aaa9da0dad87fec946c220fd1bce9c4fa7"
@@ -260,7 +260,7 @@
       "@id": "globals-training-list-300-txt",
       "name": "globals-training-list-300-txt",
       "description": "Training material ids for the 300-material subset.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/training_list_300.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/training_list_300.txt",
       "encodingFormat": "text/plain",
       "contentSize": "901 B",
       "sha256": "74da0f01bdfde6a57180f51799bf85666117e6863145dc27048bdf720954c552"
@@ -270,7 +270,7 @@
       "@id": "globals-training-list-442-txt",
       "name": "globals-training-list-442-txt",
       "description": "Training material ids for the 442-material subset.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/training_list_442.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/training_list_442.txt",
       "encodingFormat": "text/plain",
       "contentSize": "1330 B",
       "sha256": "4c8399b8daddefe8e3b4c28e8105608e4a146c0693a5e928983be77a308ad885"
@@ -280,7 +280,7 @@
       "@id": "globals-training-list-500-txt",
       "name": "globals-training-list-500-txt",
       "description": "Training material ids for the full 500-material set.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/training_list_500.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/training_list_500.txt",
       "encodingFormat": "text/plain",
       "contentSize": "1516 B",
       "sha256": "921fae5ed3429df0dd1c910294adcd287b529dba266f3c1a86ed9e26f2e4fa99"
@@ -290,7 +290,7 @@
       "@id": "globals-test-list-100-txt",
       "name": "globals-test-list-100-txt",
       "description": "Test material ids for the 100-material subset.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/test_list_100.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/test_list_100.txt",
       "encodingFormat": "text/plain",
       "contentSize": "75 B",
       "sha256": "94c42c05331ab0b9b61d80feceb351e7dea5bf44b962a494b6f453626f45383b"
@@ -300,7 +300,7 @@
       "@id": "globals-test-list-300-txt",
       "name": "globals-test-list-300-txt",
       "description": "Test material ids for the 300-material subset.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/test_list_300.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/test_list_300.txt",
       "encodingFormat": "text/plain",
       "contentSize": "225 B",
       "sha256": "cf9b7d31e305446238b82635c9da4b63c293b36907ee31847e6061a314d7691b"
@@ -310,7 +310,7 @@
       "@id": "globals-test-list-442-txt",
       "name": "globals-test-list-442-txt",
       "description": "Test material ids for the 442-material subset.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/test_list_442.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/test_list_442.txt",
       "encodingFormat": "text/plain",
       "contentSize": "331 B",
       "sha256": "fdd6e41703c405661c85dbf906ba2cc6b17be830ed7e9b10f0ada755bc7976e5"
@@ -320,7 +320,7 @@
       "@id": "globals-test-list-500-txt",
       "name": "globals-test-list-500-txt",
       "description": "Test material ids for the full 500-material set.",
-      "contentUrl": "https://huggingface.co/datasets/koalapenguin/RoboCloth/resolve/main/globals/test_list_500.txt",
+      "contentUrl": "https://huggingface.co/datasets/theialab/RoboCloth/resolve/main/globals/test_list_500.txt",
       "encodingFormat": "text/plain",
       "contentSize": "374 B",
       "sha256": "b50fd5522559e311eb5423ae673865ec9ab8e86a237ae829a8c5b27c6eaeb565"
@@ -374,6 +374,6 @@
   ],
   "sameAs": [
     "https://github.com/theialab/robocloth",
-    "https://huggingface.co/datasets/koalapenguin/RoboCloth"
+    "https://huggingface.co/datasets/theialab/RoboCloth"
   ]
 }

examples/load_material.py

@@ -19,7 +19,7 @@ import tarfile
 import numpy as np
 from huggingface_hub import hf_hub_download
 
-REPO_ID = "koalapenguin/RoboCloth"
+REPO_ID = "theialab/RoboCloth"
 REPO_TYPE = "dataset"
 
 

index/robocloth_index.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:4e872f79947eb9cf19d6405d0f37c1fbda6c25601e39643038b6332a2bbbb3c1
-size 11442842
+oid sha256:af18e6e8314a115bf30fbc4b58d489ac9bd6db090125e7f1c4b184bbec1092c9
+size 11438603

sample/README.md

@@ -1,6 +1,6 @@
 # RoboCloth — sample subset
 
-A small, self-contained slice of [RoboCloth](https://huggingface.co/datasets/koalapenguin/RoboCloth)
+A small, self-contained slice of [RoboCloth](https://huggingface.co/datasets/theialab/RoboCloth)
 for inspecting the data format without downloading terabytes.
 
 ## What is here
@@ -22,14 +22,14 @@ for inspecting the data format without downloading terabytes.
 same material at full size:
 
 ```bash
-hf download koalapenguin/RoboCloth --repo-type dataset \
+hf download theialab/RoboCloth --repo-type dataset \
     --include "globals/*" --include "materials/<id>/*" --local-dir ./RoboCloth
 ```
 
 ## Download just this subset
 
 ```bash
-hf download koalapenguin/RoboCloth --repo-type dataset \
+hf download theialab/RoboCloth --repo-type dataset \
     --include "sample/*" --include "globals/*" --include "examples/*" \
     --local-dir ./robocloth-sample
 ```