Add the DINOv3 and TIPSv2 reference features of all 200 cases

README.md

@@ -7,7 +7,7 @@ task_categories:
 
 [Project page](https://4dcodebench.com) | [Paper](https://huggingface.co/papers/2610.03715) | [Code](https://github.com/4DCodeBench/4DCodeBench)
 
-The depth, optical flow and point tracks that [4DCodeBench](https://4dcodebench.com) scores reconstructions against, so the benchmark can be evaluated without installing the models behind them. On the 100 real-world cases they are estimated from the reference video by off-the-shelf models and released here as files. On the 100 synthetic cases they are ground truth, computed from the reference world by the script in `scripts/`.
+The depth, optical flow and point tracks that [4DCodeBench](https://4dcodebench.com) scores reconstructions against, so the benchmark can be evaluated without installing the models behind them. On the 100 real-world cases they are estimated from the reference video by off-the-shelf models and released here as files. On the 100 synthetic cases they are ground truth, computed from the reference world by the script in `scripts/`. The DINOv3 and TIPSv2 features of all 200 reference videos are included as well.
 
 ## Layout
 
@@ -16,10 +16,13 @@ real/<case>/depth.h5        Video Depth Anything disparity
 real/<case>/flow.h5         RAFT optical flow
 real/<case>/tracks.npz      CoTracker3 point tracks
 real/<case>/moge.npz        MoGe-3 point map of frame 0
+{real,synthetic}/<case>/semantic_dinov3.npz    DINOv3 frame embeddings
+{real,synthetic}/<case>/semantic_tips.npz      TIPSv2 frame embeddings
+{real,synthetic}/<case>/geophys_dinov3.npz     DINOv3 pooled features for GeoPhys
 scripts/extract_synthetic.py
 ```
 
-53 GB: 46.1 GB of flow, 5.6 GB of depth, 0.9 GB of point maps and 0.2 GB of tracks. `<case>` is the case name of [Dataset-Real-World](https://huggingface.co/datasets/4DCodeBench/Dataset-Real-World).
+53 GB: 46.1 GB of flow, 5.6 GB of depth, 0.9 GB of point maps, 0.4 GB of features and 0.2 GB of tracks. `<case>` is the case name of [Dataset-Real-World](https://huggingface.co/datasets/4DCodeBench/Dataset-Real-World) or [Dataset-Synthetic](https://huggingface.co/datasets/4DCodeBench/Dataset-Synthetic).
 
 ## Real-world cases
 
@@ -40,9 +43,19 @@ The track queries are the benchmark's grid of at most 2048 points on frame 0: 51
 
 Six cases show their main object through a transparent surface: `internet_01_purple_granular_sand`, `internet_19_fabric_laundry_tumbles`, `internet_22_viscous_honey_dripping`, `phys101_01_solid_object_dropped`, `wisa_80k_06_pouring_red_wine` and `wisa_80k_46_wooden_pestle_crushing`. The video shows what lies behind the surface while a reconstruction rasterises the surface itself, so the benchmark does not score Flow, Track2D, Depth error or Uni3D MoGe on them. Their files are released for completeness only, and the models can fail on them; RAFT's flow of `wisa_80k_06_pouring_red_wine`, for one, puts hundreds of pixels of motion on its static background.
 
+## Features
+
+Semantic and GeoPhys compare a reconstruction's render with the reference video in a backbone's feature space. These files hold the reference side, for every real and synthetic case, on the case's sampled timeline (`F'` frames, see below); the render side is still embedded by the same models at scoring time.
+
+| file | model | contents |
+|---|---|---|
+| `semantic_dinov3.npz` | [DINOv3](https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m) ViT-L/16 | `embeddings (F', 1024)` float32, the final class token of each frame; `stride` |
+| `semantic_tips.npz` | [TIPSv2](https://huggingface.co/google/tipsv2-l14) L/14 | `embeddings (F', 1024)` float32, the class token of each frame; `stride` |
+| `geophys_dinov3.npz` | DINOv3 ViT-L/16 | `trajectory (F', 1024)` float32, the mean of each frame's patch tokens at block 18 of 24; `layer`, `stride` |
+
 ## Synthetic cases
 
-A synthetic case has the world it was rendered from, so its depth, flow and tracks are computed exactly rather than estimated. `scripts/extract_synthetic.py` writes them in the same layout as the real cases, from the meshes, material trajectories and camera of each world, with the same code the scorer uses to read a submission. No model checkpoint is needed.
+A synthetic case has the world it was rendered from, so its depth, flow and tracks are computed exactly rather than estimated; `synthetic/<case>/` holds only its features. `scripts/extract_synthetic.py` writes the depth, flow and tracks in the same layout as the real cases, from the meshes, material trajectories and camera of each world, with the same code the scorer uses to read a submission. No model checkpoint is needed.
 
 With the [code release](https://github.com/4DCodeBench/4DCodeBench) set up and the synthetic worlds downloaded by its `scripts/download_data.py --kind synthetic`, run from the code release's root:
 
@@ -63,8 +76,8 @@ The 8 synthetic videos longer than 300 frames are read on a thinned timeline: fr
 
 ## Using them with the benchmark
 
-The code release reads these files from `data/real/<case>/estimates/` under the same names, so copying `real/<case>/*` there lets `python -m scorer` read Flow, Track2D, Depth error and Uni3D MoGe without the estimation models. Its other reference features (DINOv3, TIPS) still come from `python -m scorer.prepare --only semantic,geophys`, whose models are on the Hugging Face Hub.
+The code release reads these files from `data/<kind>/<case>/estimates/` under the same names. Its `scripts/download_data.py --estimates` puts them there, after which the `prepare` stage can be skipped.
 
 ## Licences
 
-The real-world files are outputs of models with their own licences: Video Depth Anything Large and CoTracker3 are CC BY-NC 4.0, RAFT (torchvision) is BSD-3-Clause, and MoGe-3 is MIT. The videos they were computed from are under the terms listed in Dataset-Real-World.
+The files are outputs of models with their own licences: Video Depth Anything Large and CoTracker3 are CC BY-NC 4.0, RAFT (torchvision) is BSD-3-Clause, MoGe-3 is MIT, DINOv3 is under the DINOv3 License and TIPSv2 is Apache 2.0. The videos they were computed from are under the terms listed in Dataset-Real-World and Dataset-Synthetic.

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