Add NFRU dataset

README.md

@@ -5,6 +5,7 @@ tags:
 - neural-graphics
 - gaming
 - graphics
+- datasets
 language:
 - en
 size_categories:
@@ -15,55 +16,171 @@ size_categories:
 
 ![](https://huggingface.co/datasets/Arm/neural-graphics-dataset/resolve/main/bistro-output-colour-gamma.jpg)
 
-A collection of reference images and image sequences along with the corresponding motion, depth and other data required to train, validate and test neural super sampling algorithms.
+A compact collection of reference image sequences with accompanying motion, depth, and related rendering data, designed for training, validation, and evaluation of neural graphics models.
 
-## Dataset Details
+It is intended for use with models in the [Neural Graphics Model Gym](https://github.com/arm/neural-graphics-model-gym), including:
+
+- [Neural Super Sampling (NSS)](https://huggingface.co/Arm/neural-super-sampling)
+- [Neural Frame Rate Upscaling (NFRU)](https://huggingface.co/Arm/neural-frame-rate-upscaling)
+
+The dataset is intended as a small, practical example dataset for tutorials and experimentation. For best performance in production-level training, we recommend collecting a substantially larger dataset using a wide variety of scenes that reflect the types of content the models will be expected to handle.
+
+The model-specific sections below provide the minimum dataset sizes used to train each model.
+
+<table>
+<tr><td><strong>Curated By</strong></td><td>Arm Limited</td></tr>
+<tr><td><strong>License</strong></td><td><a href="https://huggingface.co/datasets/Arm/neural-graphics-dataset/blob/main/Arm_AI_Model_Community_License_v1_0_PRE-1154.pdf">Arm AI Model Community License</a></td></tr>
+</table>
+
+---
+
+## Neural Super Sampling (NSS)
 
 ### Dataset Description
 
-The current version of the dataset includes a limited set of data for Neural Super Sampling (see [NSS model](https://huggingface.co/Arm/neural-super-sampling) for more detail). The main purpose is to demonstrate how the [NSS model development flow](https://github.com/arm/neural-graphics-model-gym) works. It is not designed to provide a comprehensive dataset for complete model (re)training. Future releases of the [Neural Graphics Model Gym](https://github.com/arm/neural-graphics-model-gym) will provide tools to capture and convert content for use in model training and retraining. 
+The current dataset includes a compact collection of data for [NSS](https://huggingface.co/Arm/neural-super-sampling). Its primary purpose is to demonstrate the NSS model development workflow in the [Neural Graphics Model Gym](https://github.com/arm/neural-graphics-model-gym). To train NSS, we use a dataset containing more than 50,000 frames. In general, larger datasets improve machine learning model performance.
 
-- **Curated by:** Arm Limited
-- **License:** [Arm_AI_Model_Community_License](https://huggingface.co/datasets/Arm/neural-graphics-dataset/blob/main/Arm_AI_Model_Community_License_v1_0_PRE-1154.pdf)
+A [plugin](https://github.com/arm/neural-graphics-data-capture-for-unreal) for capturing data in Unreal® Engine is available to capture your own content for use in (re)training or evaluation in the Neural Graphics Model Gym.
 
 ### Dataset Sources
 
-The dataset was captured from [Amazon Lumberyard Bistro](http://developer.nvidia.com/orca/amazon-lumberyard-bistro) scene implemented in [Unreal® Engine](https://www.unrealengine.com/)
+The NSS dataset was captured from the [Amazon Lumberyard Bistro](http://developer.nvidia.com/orca/amazon-lumberyard-bistro) scene implemented in [Unreal Engine](https://www.unrealengine.com/).
 - **Original asset location:** [Morgan McGuire, Computer Graphics Archive, July 2017](https://casual-effects.com/g3d/data10/research/model/bistro/Exterior.zip)
 
-## Dataset Structure
+### Dataset Structure
 
-The dataset is in a shape of [Safetensors](https://huggingface.co/docs/safetensors/en/index) files for training, validataion and testing:
+The NSS dataset uses sequence-level [Safetensors](https://huggingface.co/docs/safetensors/en/index) files for training, validation, and testing. Each file stores one full captured sequence rather than one single frame.
 
 ```
-neural-graphics-dataset /
-├── test/ # test set
-├── train/ # training set
-└── val/ # validation set
+neural-graphics-dataset/
+└── nss/
+  ├── test/   # test set
+  ├── train/  # training set
+  └── val/    # validation set
 ```
 
-Each Safetensor file contains the following data for training Neural Super Sampling:
+### Safetensors Contents
+
+Each NSS Safetensors file contains the following data:
+
+| Tensor                | Description                                 |
+|-----------------------|---------------------------------------------|
+| `colour_linear`       | Low-resolution color data in linear format  |
+| `ground_truth_linear` | High-resolution color data in linear format |
+| `depth`               | Per-pixel depth data                        |
+| `depth_params`        | Additional depth parameters                 |
+| `motion_lr`           | Low-resolution motion vectors               |
+| `motion`              | High-resolution motion vectors              |
+| `exposure`            | Exposure values                             |
+| `jitter`              | Jitter vector                               |
+| `render_size`         | Low-resolution image shape                  |
+| `zNear`               | Camera near plane values                    |
+| `zFar`                | Camera far plane values                     |
 
-- `colour_linear` - low resolution color data in linear format
-- `ground_truth_linear` - high resolution color data in linear format 
-- `depth` - per-pixel depth data
-- `depth_params` - additional depth parameters
-- `motion_lr` - low resolution motion vectors
-- `motion` - high resolution motion vectors
-- `exposure` - exposure values
-- `jitter` - jitter vector
-- `render_size` - low resolution image shape
-- `zNear` - camera near plane values
-- `zFar` - camera far plane values
+The training and validation sets use cropped, reduced-resolution data, while the test set is provided at the original capture resolution.
 
-## Bias, Risks, and Limitations
+### Bias, Risks, and Limitations
 
 The training set contains a limited number of frames to be used as training data. It is intended for use with [NSS tutorials](https://github.com/arm/neural-graphics-model-gym-examples) and experimentation with the [NSS algorithm](https://github.com/arm/neural-graphics-model-gym).
 
+### Related Resources
+
+A pre-trained Neural Super Sampling model can be accessed from the [NSS model card](https://huggingface.co/Arm/neural-super-sampling).
+
+---
+
+## Neural Frame Rate Upscaling (NFRU)
+
+### Dataset Description
+
+The current dataset includes a compact collection of data for [NFRU](https://huggingface.co/Arm/neural-frame-rate-upscaling). Its primary purpose is to demonstrate the NFRU model development workflow in the [Neural Graphics Model Gym](https://github.com/arm/neural-graphics-model-gym). To train NFRU, we use a dataset containing more than 16,000 frames. In general, larger datasets improve machine learning model performance.
+
+Future releases of the Neural Graphics Model Gym will provide tools to capture and convert content for use in model training and retraining.
+
+### Dataset Sources
+
+The NFRU dataset was captured from the [Amazon Lumberyard Bistro](http://developer.nvidia.com/orca/amazon-lumberyard-bistro) scene implemented in [Unreal Engine](https://www.unrealengine.com/).
+- **Original asset location:** [Morgan McGuire, Computer Graphics Archive, July 2017](https://casual-effects.com/g3d/data10/research/model/bistro/Exterior.zip)
+
 
-## More Information
+### Dataset Structure
 
-A pre-trained Neural Super Sampling model can be accessed from the [NSS model card](https://huggingface.co/Arm/neural-super-sampling) 
+The NFRU dataset uses sequence-level [Safetensors](https://huggingface.co/docs/safetensors/en/index) files for training, validation, and testing. Each file stores one full captured sequence rather than one single frame.
+
+```text
+neural-graphics-dataset/
+└── nfru/
+  ├── test/   # test set
+  ├── train/  # training set
+  └── val/    # validation set
+```
+
+### Safetensors Contents
+
+Each NFRU Safetensors file contains the following data:
+
+| Tensor                         | Description                                                                |
+|--------------------------------|----------------------------------------------------------------------------|
+| `rgb_linear`                   | High-resolution linear RGB frames                                          |
+| `rgb_reinhard`                 | High-resolution Reinhard-tonemapped RGB frames                             |
+| `depth`                        | Low-resolution depth buffer                                                |
+| `DepthParams`                  | Depth reprojection parameters                                              |
+| `NearPlane`                    | Camera near plane values                                                   |
+| `FarPlane`                     | Camera far plane values                                                    |
+| `infinite_zFar`                | Flag indicating the original capture used an infinite far plane            |
+| `FovX`                         | Horizontal camera field of view in radians                                 |
+| `FovY`                         | Vertical camera field of view in radians                                   |
+| `ViewProj`                     | 4x4 view-projection matrix                                                 |
+| `InverseY`                     | Flag indicating whether Y inversion should be applied to camera transforms |
+| `exposure`                     | Exposure values                                                            |
+| `jitter`                       | Jitter offsets                                                             |
+| `render_size`                  | Render resolution                                                          |
+| `img_id`                       | Frame index within the sequence                                            |
+| `seq_id`                       | Sequence identifier                                                        |
+| `mv_{}_f30_m1`                 | Motion vectors to the previous 30 FPS capture                              |
+| `mv_{}_f60_m1`                 | Motion vectors to the previous 60 FPS frame                                |
+| `sy_{}_f30_m1`                 | Synthetic motion vectors to the previous 30 FPS capture                    |
+| `sy_{}_f30_p1`                 | Synthetic motion vectors to the next 30 FPS capture                        |
+| `sy_{}_f60_m1`                 | Synthetic motion vectors to the previous 60 FPS frame                      |
+| `sy_{}_f60_p1`                 | Synthetic motion vectors to the next 60 FPS frame                          |
+| `dilated_sy_{}_f30_m1`         | Dilated synthetic motion vectors to the previous 30 FPS capture            |
+| `dilated_sy_{}_f30_p1`         | Dilated synthetic motion vectors to the next 30 FPS capture                |
+
+#### Tensor Naming Convention
+
+NFRU frame indices are measured on a 60 FPS timeline and use an interpolation triplet:
+
+- `m1`: previous input frame
+- `t`: frame to be predicted
+- `p1`: next input frame
+
+For example, if `t` is frame `0004`, then:
+
+- `m1` is frame `0003`
+- `p1` is frame `0005`
+
+Motion-like tensors (`mv_*`, `sy_*`) also include an `f60` or `f30` tag:
+
+- `f60`: motion between adjacent 60 FPS frames
+- `f30`: motion between adjacent 30 FPS captures
+
+
+| 60 FPS frame index | `0000` | `0001` | `0002` | `0003` | `0004` | `0005` | `0006` |
+|--------------------|--------|--------|--------|--------|--------|--------|--------|
+| 60 FPS capture     |   x    |   x    |   x    |   x    |   x    |   x    |   x    |
+| 30 FPS capture     |   x    |        |   x    |        |   x    |        |   x    |
+
+In the stored Safetensors files, `{}` represents the source frame index. For example, if the source frame is `0004`, then `sy_{}_f30_m1` refers to motion associated with frame `0002`, while `sy_{}_f30_p1` refers to motion associated with frame `0006`.
+
+### Bias, Risks, and Limitations
+
+The training set contains a limited number of frames to be used as training data. It is intended for use with [NFRU tutorials](https://github.com/arm/neural-graphics-model-gym-examples) and experimentation with the [NFRU algorithm](https://github.com/arm/neural-graphics-model-gym).
+
+### Related Resources
+
+A pre-trained Neural Frame Rate Upscaling model can be accessed from the [NFRU model card](https://huggingface.co/Arm/neural-frame-rate-upscaling).
+
+---
 
 ## License
 
@@ -71,6 +188,6 @@ The Neural Graphics Dataset is released under [Arm AI Model Community License](h
 
 ## Trademark notice
 
-Arm® is a registered trademarks of Arm Limited (or its subsidiaries) in the US and/or elsewhere.
+Arm® is a registered trademark of Arm Limited (or its subsidiaries) in the US and/or elsewhere.
 
-Unreal, Unreal Engine, and the Unreal Engine logo are trademarks or registered trademarks of Epic Games, Inc. in the USA and elsewhere.
+Unreal® is a trademark or registered trademark of Epic Games, Inc. in the United States of America and elsewhere.

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