Card: results of the paper (Table 2) incl. VPM, splits and hard clips as in the paper, citation

README.md

@@ -25,8 +25,7 @@ size_categories:
 # VP-WorldBench
 
 VP-WorldBench is a **multi-view 4D benchmark for video world models**, created as part of the **Video-Point Model (VPM)**
-project ([paper](https://arxiv.org/abs/2610.06940) · [project page](https://rootyjeon.github.io/video-point-model/) ·
-[code](https://github.com/vpm-project/VPM)). It tests whether generated videos stay consistent across views and move
+project ([project page](https://rootyjeon.github.io/video-point-model/) · [code](https://github.com/vpm-project/VPM)). It tests whether generated videos stay consistent across views and move
 like the real 3D scene.
 
 It has 1,200 synchronized multi-view clips in four subsets: simulated and real, robot and general scenes. Every clip
@@ -46,14 +45,15 @@ instant.*
 
 ## Subsets
 
-| subset | source | clips | views | fps | resolution | frames / clip | 3D points / clip | depth |
-|:--|:--|--:|--:|--:|:--|:--|:--|:--|
-| **Simul-Robot** | Unreal Engine, GR1T2 humanoid tabletop pick & place | 500 | 3 | 20 | 1280×1280 | 81–680 | 768–1,536 | rendered |
-| **Simul-General** | CARLA, pedestrians (332) and vehicles (168) seen by following cameras | 500 | 4 | 15 | 1280×1280 | 81–181 | 256 | rendered |
-| **Real-Robot** | RB-Y1 dual-arm robot with dexterous hands, teleoperated, ego + exo cameras | 100 | 2 | 15 | 1280×720 | 139–325 | 768 | – |
-| **Real-General** | one person in a 47-camera capture studio, 4 selected views | 100 | 4 | 15 | 1024×768 | 150 | 937–1,061 | reconstruction |
+| subset | source | clips | hard | views | fps | resolution | frames / clip | 3D points / clip | depth |
+|:--|:--|--:|--:|--:|--:|:--|:--|:--|:--|
+| **Simul-Robot** | Unreal Engine, GR1T2 humanoid tabletop pick & place: 436 at new locations in the training city, 64 in two unseen kitchens | 500 | 27 | 3 | 20 | 1280×1280 | 81–680 | 768–1,536 | rendered |
+| **Simul-General** | CARLA, pedestrians (332) and vehicles (168) seen by following cameras, in a different city from the urban training data | 500 | 95 | 4 | 15 | 1280×1280 | 81–181 | 256 | rendered |
+| **Real-Robot** | RB-Y1 dual-arm robot with dexterous hands, teleoperated, ego + exo cameras | 100 | – | 2 | 15 | 1280×720 | 139–325 | 768 | – |
+| **Real-General** | one person in a 48-camera capture studio, 4 selected views | 100 | – | 4 | 15 | 1024×768 | 150 | 937–1,061 | reconstruction |
 
-All clips are test clips.
+All clips are test clips. In a **hard** clip (`difficulty: hard` in `index.json`) individual cameras lose sight of the
+target for at least 30 consecutive frames while at least one camera sees it at every frame.
 
 ## Ground truth
 
@@ -144,37 +144,44 @@ every view. The generated views are scored on three axes:
 - **appearance:** PSNR, SSIM, LPIPS and FVD;
 - **cross-view consistency:** Cross-view MEt3R. This is the distance between the generated clip's cross-view MEt3R
   and the real video's, averaged over clips. Each clip's MEt3R is the mean over all view pairs and timesteps.
-- **3D motion accuracy:** δ. The generated views are lifted to 3D tracks (MVTracker + MoGe-2) and compared with the GT
-  tracks:
-  - δ_avg counts every frame of every point;
-  - δ mov. counts only the frames where the GT point has moved, so a static video scores 0.
+- **3D motion accuracy:** δ3D avg. The generated views are lifted to 3D tracks (MVTracker + MoGe-2) and compared with
+  the GT tracks. The toolkit also reports δ mov., which counts only the frames where the GT point has moved.
 
 The protocol can be changed: reference frame, I2V/V2V context length, horizon, frame step, views and resolution.
 
-Image-to-video baselines are scored on all clips. Each view is generated independently from its own first frame.
-Cross-view MEt3R is lower-better; the real-video row shows its raw MEt3R.
-
-| subset | method | FVD ↓ | PSNR ↑ | Cross-view MEt3R ↓ | δ_avg ↑ | δ mov. ↑ |
-|:--|:--|--:|--:|--:|--:|--:|
-| Simul-Robot | *real video / frozen points* | – | – | (0.428) | 60.7 / 84.5 | 22.0 / 0.0 |
-| | HunyuanVideo-1.5 (480p I2V, step-distilled) | 98.3 | 20.19 | 0.022 | 45.3 | 4.8 |
-| | Wan2.2-TI2V-5B | 314.8 | 15.79 | 0.108 | 32.2 | 2.9 |
-| | Cosmos3-Nano | 104.6 | 20.02 | 0.040 | 43.3 | 6.4 |
-| Simul-General | *real video / frozen points* | – | – | (0.482) | 60.1 / 7.3 | 55.7 / 0.0 |
-| | HunyuanVideo-1.5 (480p I2V, step-distilled) | 115.8 | 11.45 | 0.061 | 25.2 | 17.8 |
-| | Wan2.2-TI2V-5B | 206.1 | 10.43 | 0.076 | 19.9 | 12.2 |
-| | Cosmos3-Nano | 167.4 | 10.99 | 0.062 | 24.3 | 17.0 |
-| Real-Robot | *real video / frozen points* | – | – | (0.499) | 75.1 / 61.9 | 48.2 / 0.0 |
-| | HunyuanVideo-1.5 (480p I2V, step-distilled) | 426.4 | 16.03 | 0.102 | 34.6 | 24.3 |
-| | Wan2.2-TI2V-5B | 550.5 | 14.58 | 0.159 | 26.5 | 13.6 |
-| | Cosmos3-Nano | 326.1 | 16.36 | 0.099 | 37.2 | 16.1 |
-| Real-General | *real video / frozen points* | – | – | (0.591) | 68.0 / 37.6 | 48.0 / 0.0 |
-| | HunyuanVideo-1.5 (480p I2V, step-distilled) | 94.7 | 20.75 | 0.014 | 29.9 | 3.6 |
-| | Wan2.2-TI2V-5B | 122.6 | 17.46 | 0.030 | 28.2 | 4.0 |
-| | Cosmos3-Nano | 56.2 | 21.80 | 0.015 | 31.9 | 3.9 |
-
-Full tables, with SSIM, LPIPS, AJ, OA, survival, ADE, standard errors and the Cross-view MEt3R floors, are in the
-[toolkit README](https://github.com/vpm-project/VPM/tree/main/vp-worldbench).
+The results of the paper (Table 2). Image-to-video: every model receives one frame of each view and generates the next
+64 frames, resized to 480p. Single-view models generate each view independently. FVD and PSNR are computed per view and
+averaged over the views. **MV**: the model generates all views jointly. VPM (V) is trained on multi-view videos only,
+VPM (V+T) also on 3D point trajectories. X-WAM is evaluated on the robot subsets only.
+
+| subset | method | MV | FVD ↓ | PSNR ↑ | Cross-view MEt3R ↓ | δ3D avg ↑ |
+|:--|:--|:-:|--:|--:|--:|--:|
+| **Simul-Robot** (500) | HunyuanVideo-1.5 | ✗ | 98.34 | 20.21 | 0.023 | 41.62 |
+| | Wan2.2-5B | ✗ | 315.13 | 15.84 | 0.108 | 29.51 |
+| | X-WAM | ✓ | 203.83 | 19.97 | 0.038 | 38.80 |
+| | Cosmos3-Nano | ✗ | 104.74 | 20.05 | 0.040 | 39.03 |
+| | VPM (V) | ✓ | 78.52 | 21.41 | 0.022 | 43.62 |
+| | **VPM (V+T)** | ✓ | **67.35** | **22.08** | **0.016** | **50.05** |
+| **Simul-General** (500) | HunyuanVideo-1.5 | ✗ | 115.63 | 11.45 | 0.061 | 23.10 |
+| | Wan2.2-5B | ✗ | 205.73 | 10.43 | 0.076 | 18.00 |
+| | Cosmos3-Nano | ✗ | 167.02 | 10.99 | 0.062 | 22.12 |
+| | VPM (V) | ✓ | 101.37 | 16.95 | 0.057 | 28.71 |
+| | **VPM (V+T)** | ✓ | **81.92** | **19.16** | **0.050** | **36.80** |
+| **Real-Robot** (100) | HunyuanVideo-1.5 | ✗ | 426.76 | 16.04 | 0.102 | 36.17 |
+| | Wan2.2-5B | ✗ | 550.55 | 14.58 | 0.159 | 29.65 |
+| | X-WAM | ✓ | 492.31 | 17.63 | 0.091 | 50.86 |
+| | Cosmos3-Nano | ✗ | 326.11 | 16.37 | 0.099 | 41.50 |
+| | VPM (V) | ✓ | 296.83 | 18.42 | 0.074 | 50.83 |
+| | **VPM (V+T)** | ✓ | **255.19** | **20.57** | **0.064** | **64.75** |
+| **Real-General** (100) | HunyuanVideo-1.5 | ✗ | 105.83 | 20.00 | 0.014 | 21.35 |
+| | Wan2.2-5B | ✗ | 115.52 | 16.97 | 0.030 | 16.82 |
+| | Cosmos3-Nano | ✗ | 66.24 | 20.89 | 0.015 | 20.47 |
+| | VPM (V) | ✓ | 62.85 | 20.96 | 0.010 | 26.54 |
+| | **VPM (V+T)** | ✓ | **56.48** | **22.10** | **0.007** | **33.92** |
+
+The toolkit's default protocol `vpm-v1` differs from the paper's evaluation in some details, so its scores are not
+identical to this table. Its outputs also include SSIM, LPIPS, δ mov., AJ, OA, survival, ADE, standard errors and the
+Cross-view MEt3R floors; see the [toolkit README](https://github.com/vpm-project/VPM/tree/main/vp-worldbench).
 
 **Further tasks on the same data**, each with input export, baselines and an evaluator in the toolkit:
 
@@ -195,7 +202,7 @@ Full tables, with SSIM, LPIPS, AJ, OA, survival, ADE, standard errors and the Cr
 | Simul-Robot | renders of humanoid (Fourier GR1T2) pick & place episodes in Unreal Engine scenes, produced for this benchmark |
 | Simul-General | CARLA renders of pedestrians and vehicles with target-following cameras, produced for this benchmark |
 | Real-Robot | the RB-Y1 WujiHand2 teleoperation dataset (100 episodes, ego + exo), with 3D arm and hand point tracks from forward kinematics; videos and tracks time-aligned for this benchmark |
-| Real-General | 100 ten-second clips of an in-house 47-camera capture, with surface and body-model tracks, reconstruction depth, SAM2 person masks, 2D/3D keypoints and motion captions produced for this benchmark |
+| Real-General | 100 ten-second clips of an in-house 48-camera capture, with surface and body-model tracks, reconstruction depth, SAM2 person masks, 2D/3D keypoints and motion captions produced for this benchmark |
 
 The Real-General captions were written by vision-language-model annotators and checked by model verifiers.
 `Real-General/_source/CAPTIONS.md` gives the process and the measured accuracy.
@@ -207,13 +214,14 @@ maintainer: Byungwoo Jeon, imbw2024@kaist.ac.kr.
 ## Citation
 
 ```bibtex
-@article{vpm2026,
-  title   = {Learning the 4D World through Point Trajectory Forecasting},
-  author  = {Jeon, Byungwoo and Hong, Sunghwan and Kim, Dohyeon and Lee, Jonghoon and Park, Jungwoo and Lee, Inhee and
-             Baek, Doosan and Kim, Jinil and Lim, Taehyun and Joo, Hanbyul and Pollefeys, Marc and Xie, Saining and
-             Shin, Jinwoo},
-  journal = {arXiv preprint arXiv:2610.06940},
-  year    = {2026}
+@misc{vpm2026,
+  title  = {Learning the 4D World through Point Trajectory Forecasting},
+  author = {Jeon, Byungwoo and Hong, Sunghwan and Kim, Dohyeon and Lee, Jonghoon and Park, Jungwoo and Lee, Inhee and
+            Baek, Doosan and Kim, Jinil and Lim, Taehyun and Joo, Hanbyul and Pollefeys, Marc and Xie, Saining and
+            Shin, Jinwoo},
+  year   = {2026},
+  note   = {Preprint},
+  url    = {https://rootyjeon.github.io/video-point-model/}
 }
 ```