UnrealFall is a novel framework developed within Unreal Engine 5, designed to generate high-quality synthetic videos. It uses generative motion models and Gaussian Splatting technology to create hyper-realistic virtual environments. Our framework seamlessly integrates animations into Unreal Metahumans, allowing for extensive and varied data generation, which can be exploited to address the previously introduced data limitations.
A baseline dataset has been created depicting falls experienced by elderly people in different situations. To this end, custom avatars with unique physical characteristics are generated. These avatars are captured in different 3D scenes with multiple virtual cameras, allowing to recreate a variety of realistic indoor and outdoor scenes. The resultant dataset comprises 250 four-second videos in RGB-D and segmentation formats, showcasing the framework's capability to generate realistic and diverse data.
To load Gaussian Point Clouds the LumaAI software and plugin has been used. This plugin is already installed in the project. The individual scenes must be saved in .uasset format in the Content > Scenes folder. You can find examples in the data release.
The motion-diffusion-model has been used to generate human animations from text prompts. The output of the model is a skeleton in 24-joint SMPL format. This data is saved in a .pkl file.
Afterwards, a script must be used to convert this animation to .fbx format. In our case we have used SMPL-to-FBX. The IKRetargeter component (named RTG_SMPL_FBX_Mannequin in the project) has to be configured in the Unreal Engine project to match the bones of the imported SMPL skeleton with those of the MetaHuman.
The project includes the assets of 4 Metahumans, but the assets of the face are missing as they take up a lot of space, these files are in a release.
Warning
Please note that the Unreal axes and the outputs generated with the models may vary and therefore the front axis must be configured when importing the FBX.
The C++ component HSMTracker has been created to generate videos from virtual cameras. An instance of this class has to be created within the level to be able to generate data.
Use of HSMTracker The instance has to be parameterised with the correct paths and data of the images to be generated.
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Pawns: List of Pawns reference which are used in the enviroment.
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CameraActors: List of Cameras used to record the scene.
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bRecordMode:
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- TRUE: To debug
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- FALSE: To generate videos from the cameras
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scene_save_directory: Root directory of the project.
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scene_folder: Folder where scenes definition JSONs are stored e.g. "MotionData".
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json_file_names: List of JSON file names (without extension) to be runned.
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start_frames: List of start rebuild frames for the corresponding sequence from the previous sequence list.
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generate_rgb: If checked, RGB images (JPG RGB 8bit) will be generated for each frame of rebuilt sequences.
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format_rgb: Format for RGB images (PNG ~3MB, JPG 95% ~800KB, JPG 80% ~120KB).
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generate_depth: If checked, Depth images (PNG Gray 16bit) will be generated for each frame of rebuilt sequences.
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generate_object_mask: If checked, Human Mask images (PNG RGB 8bit) will be generated for each frame of rebuilt sequences.
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screenshots_save_directory: Directory where the folder for storing generated images from rebuilt sequences will be created.
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screenshots_folder: Folder where generated images from rebuilt sequences will be stored. e.g. "Screenshots".
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screenshot_width: Width size for generated images.
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screenshot_height: Height size for generated images.
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initial_delay: Seconds to wait since execution starts until rebuild process does.
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place_cameras_delay: Seconds to wait since skeletons are placed until cameras can be placed (needed for avoiding failures with bone cameras positions, 0.1 is enough).
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first_viewmode_of_frame_delay: Seconds to wait since rebuild is done until first camera is set and viewmode is changed (0.1 is enough).
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change_viewmode_delay: Seconds to wait since last image is generated until next viewmode can be changed (0.1 is enough, 0.2 is also good for slower PCs).
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take_screenshot_delay: Seconds to wait since viewmode has changed until image is generated (0.1 is enough).
Inside the MotionData folder you will find the JSON with the information of each sequence. This is the format:
{
"name": "Sequence name",
"total_frames": Nº Frames,
"animations": [
{"name":"/Script/Engine.AnimSequence'/Game/Motion/Path_Of_Animation_Asset.'"}
],
"cameras": [
{
"name": "CameraName",
"stereo": 0,
"fov": 90,
"position":
{
"x": 622.6,
"y": 36.76,
"z": 172.92
},
"rotation":
{
"r": -4.16,
"p": -38.427,
"y": -80.77
}
}, ...
],
"skeletons": [
{
"name": "Name_of_Metahuman_Assset",
"num_bones": 24,
"position":
{
"x": 592.25,
"y": -351.26,
"z": -10
},
"rotation":
{
"r": 0,
"p": 0,
"y": 150
}
}
]
}
The dataset is composed of 250 videos of 4 seconds each. The videos are in RGB-D format and human segmentation. It can be downloaded from the data release.
The format of the name of the videos is the following: sX_aName_Gender_MHId_Format_CameraN
- X: Scene Id
- aName: Id the motion sequence.
- Gender: F (Female) or M (Male)
- MHId: Id of the Metahuman
- Format: RGBD, Depth or Segmentation
- CameraN: Camera Id
Scene information
| Scene Id | Scene Name |
|---|---|
| s0 | Meeting room |
| s1 | Living room |
| s2 | Living room |
| s3 | Study room |
| s4 | Kitchen |
| s5 | Living room |
| s6 | Leisure room |
| s7 | Park |
If you find this work useful for your research, please cite:
@inproceedings{UnrealFall,
title={UnrealFall: Overcoming Data Scarcity through Generative Models},
author={David Mulero-Pérez and Manuel Benavent-Lledo and David Ortiz-Perez and Jose Garcia-Rodriguez},
booktitle={IJCNN},
year={2024}
}