Card review pass: released-baseline framing, indoor confound named, CI scope, dual-track license, ten-modality and rights-chain context, planned experiments

README.md

@@ -2,14 +2,14 @@
 license: other
 license_name: originlab-noncommercial-research
 license_link: LICENSE.md
-extra_gated_heading: "Request access to OriginLab Game-Depth"
-extra_gated_prompt: "By requesting access you agree to the CC-BY-NC-4.0 license plus a model-release requirement: use is for non-commercial research only, and any model trained on or derived from this data must be publicly released with open weights and a model card. Commercial use requires a separate agreement with OriginLab."
+extra_gated_heading: "Request access to Origin Lab Game-Depth"
+extra_gated_prompt: "Two access tracks (see LICENSE.md). Research License: non-commercial research only, and any model trained on or derived from this data must be publicly released with open weights and a model card. Internal Evaluation License: 90-day internal evaluation with no publication or release obligation and no deployment. Commercial use requires a separate agreement with Origin Lab."
 extra_gated_fields:
   Name: text
   Affiliation: text
   Intended use: text
-  "I will use this dataset for non-commercial research only": checkbox
-  "I will publicly release any model I train on this dataset": checkbox
+  "License track requested (research / internal evaluation)": text
+  "I agree to the terms of my requested license track": checkbox
 task_categories:
 - depth-estimation
 - image-to-image
@@ -22,7 +22,7 @@ tags:
 - pretraining
 size_categories:
 - 10K<n<100K
-pretty_name: OriginLab Game-Depth (RGB + Dense Z-Buffer)
+pretty_name: Origin Lab Game-Depth (RGB + Dense Z-Buffer)
 configs:
 - config_name: default
   data_files:
@@ -38,10 +38,18 @@ configs:
   <img src="https://huggingface.co/datasets/originlab/game-depth/resolve/main/assets/logo.png" alt="OriginLab" width="320">
 </p>
 
-# OriginLab Game-Depth: RGB + Dense Z-Buffer Depth
+# Origin Lab Game-Depth: RGB + Dense Z-Buffer Depth
 
 Dense depth from game engines, as a scalable substitute for scarce real depth ground truth.
 
+Depth is one of ten frame-locked modalities Origin Lab captures in-engine (pre- and post-HUD RGB, depth,
+surface normals, camera pose, keyboard/mouse inputs, in-engine events, game state, audio, and per-frame
+training tables) - this release isolates the depth channel; the full multimodal corpus is
+[`originlab/game-recordings-v3`](https://huggingface.co/datasets/originlab/game-recordings-v3). All
+gameplay is captured under non-exclusive licenses with the rights holders by consenting, compensated
+players. The engine measures absolute geometry: this release ships relative log-nearness, and metric depth
+is the next release.
+
 Website: [originlab.ai](https://originlab.ai)
 
 **Data:** this repo (load with `load_dataset("originlab/game-depth")`).<br>
@@ -54,7 +62,9 @@ or indoor-only, and purpose-built synthetic datasets are expensive and narrow. G
 a dense, exact z-buffer for every frame, for free. We ask whether that signal can stand in for real data.
 Training a depth model from scratch on ~17.8k game frames, roughly a quarter of the synthetic corpus behind
 the Lotus baseline, we find it transfers to real outdoor scenes better than that baseline (KITTI AbsRel
-0.191 vs 0.224), while indoor scenes remain its frontier. The reason is geometric rather than cosmetic: the
+0.191 vs 0.224). The comparison is against the publicly released Lotus checkpoint, trained by its authors
+under their own schedule - a released-baseline comparison, not a controlled retrain. Indoor scenes remain
+this dataset's frontier. The reason is geometric rather than cosmetic: the
 game corpus teaches an outdoor ground-plane structure that real driving data shares, even though its pixels
 look nothing alike. We therefore position game z-buffers as a scalable pre-training substrate, a cheap
 geometric prior for initializing models before fine-tuning on limited real data, rather than a replacement
@@ -76,8 +86,8 @@ Splits (48,615 frames total):
 
 - `train` (17,799) : the session-capped, stride-sampled curated training split used in the results below.
 - `test` (999) : session-disjoint held-out test set.
-- `extra` (29,815) : the remaining frames (these share sessions with `train`); released so you can build
-  your own curation instead of ours.
+- `extra` (29,815) : the remaining frames. Do not evaluate on `extra` - it shares sessions with `train`.
+  Released so you can build your own curation instead of ours.
 
 ### Usage
 
@@ -144,7 +154,11 @@ of the diffusion-family models:
 | Depth-Anything-V2 (real-data reference) | 0.075 | 0.947 |
 
 The gap is statistically clear, not noise: 95% bootstrap confidence intervals are [0.189, 0.194] for ours
-and [0.221, 0.226] for Lotus, which do not overlap.
+and [0.221, 0.226] for Lotus, which do not overlap. These intervals cover test-set sampling only, not
+run-to-run training variance; all results are single-seed. One confound should be named plainly: Lotus
+trains on 54k indoor frames plus 20k driving-sim frames while our training data is 0% indoor, so this
+result is equally consistent with "domain match wins" as with "game data wins" - the outdoor-only control
+(ours vs Virtual KITTI alone at matched size, Section 7) will settle which.
 
 A model that has only ever seen rendered game frames predicts real outdoor depth more accurately than one
 trained on purpose-built synthetic data, and it does so on real photographs, which tells us synthetic-RGB
@@ -221,7 +235,7 @@ matched, fair numbers.
 
 ![NYU predictions, both models fine-tuned](figures/nyu_samples.png)
 
-NYU predictions with both models fine-tuned on real NYU under the identical recipe (selected examples).
+NYU predictions with both models fine-tuned on real NYU under an identical fine-tune recipe applied to both initializations (selected examples).
 The game-pretrained model produces indoor depth as close to the ground truth as the fine-tuned Lotus
 baseline, consistent with the tied metrics above.
 
@@ -248,8 +262,10 @@ existing models have not already absorbed.
 
 ## 7. Where this goes (v0.3.0)
 
-- Scale: more frames and denser sampling, to test whether the outdoor margin widens (a data-scaling study
-  measures whether the task is still data-limited).
+- Data-scaling curve: accuracy across roughly 2k to 48.6k frames, step-matched - the direct test of
+  whether accuracy is still climbing with capture.
+- Outdoor-only synthetic control: ours vs Virtual KITTI alone at matched size, to separate "domain match
+  wins" from "game data wins" on KITTI.
 - Coverage: indoor and more varied scenes, to convert the indoor frontier into a strength.
 - Causal test: a depth-corruption ablation (holding RGB fixed, destroying the depth geometry, and measuring
   the drop in transfer) to move the geometry mechanism from correlation to causation.
@@ -287,9 +303,12 @@ Full detail in [`METHODOLOGY.md`](METHODOLOGY.md); machine-readable metrics (inc
 [`results/`](results/). In brief: all models run through one harness with per-model output conventions
 handled explicitly, and predictions aligned to ground truth by least-squares scale-shift. The harness is
 validated by Depth-Anything-V2 reproducing its published NYU number (about 0.055). KITTI is processed at
-native resolution, because its roughly 3.4:1 frames are otherwise squashed and blurred. The controlled
-comparison is ours vs Lotus (identical recipe, only the data differs); Marigold and Depth-Anything-V2 are
-external checkpoints included as reference points, with inference settings disclosed. Depth only; normals
+native resolution for every model identically, because its roughly 3.4:1 frames are otherwise squashed and
+blurred. Ours shares the Lotus architecture and training recipe, but the headline comparison is against the
+publicly released Lotus checkpoint trained by its authors under their own schedule - we did not retrain
+Lotus, so this is a released-baseline comparison, not a controlled same-recipe experiment. Marigold and
+Depth-Anything-V2 are external checkpoints included as reference points, with inference settings
+disclosed. Depth only; normals
 are out of scope. Point estimates are single-seed; multi-seed variance is noted as future work in Section 7.
 
 Training footprint: the trained component is the SD2-base UNet (about 0.87B trainable parameters; VAE and
@@ -301,7 +320,17 @@ is that smaller fractions imply more passes over the data (a memorization caveat
 
 ## License
 
-Released for **non-commercial research** under the OriginLab Game-Depth License ([`LICENSE.md`](LICENSE.md)), with a **model-release requirement** (any model trained on this data must be publicly released with open weights and a model card). No redistribution of the raw data without consent; commercial or production use requires a separate agreement (contact Origin Lab at https://app.originlab.ai). Access is gated - accept the terms to download.
+Two access tracks, both gated ([`LICENSE.md`](LICENSE.md)):
+
+- **Internal Evaluation License**: 90-day internal evaluation - no publication or release obligation, no
+  deployment or production use. Built so a research team can test the signal quietly and convert
+  commercially.
+- **Research License**: non-commercial research with a **model-release requirement** (any model trained on
+  this data must be publicly released with open weights and a model card).
+
+No redistribution of the raw data without consent; commercial or production use requires a separate
+agreement (contact Origin Lab at https://app.originlab.ai). Access is gated - request the track you need in
+the access form.
 
 ## References
 
@@ -327,7 +356,7 @@ Released for **non-commercial research** under the OriginLab Game-Depth License
 
 ```bibtex
 @misc{originlab2026gamedepth,
-  title  = {OriginLab Game-Depth: RGB + Dense Z-Buffer Depth},
+  title  = {Origin Lab Game-Depth: RGB + Dense Z-Buffer Depth},
   author = {Origin Lab},
   year   = {2026},
   url    = {https://app.originlab.ai}