| --- |
| license: mit |
| tags: |
| - computer-vision |
| - pytorch |
| - volumetric-video |
| - multimedia-systems |
| --- |
| |
| <div align="center"> |
|
|
| # ReVo: A Cross-Layer Reliable Volumetric Videoconferencing System |
|
|
| [](https://umassos.github.io/revo-website/) |
|
|
| **Ankur Aditya**<sup>1*</sup>, **Diptyaroop Maji**<sup>1*</sup>, **Lingdong Wang**<sup>1</sup>, **Bhavya Ramakrishna**<sup>2</sup>, **Ramesh Sitaraman**<sup>1,3</sup>, **Prashant Shenoy**<sup>1</sup> |
|
|
| <sup>1</sup>University of Massachusetts Amherst <sup>2</sup>Dolby Labs <sup>3</sup>Akamai Tech |
| <br> |
| <sup>*</sup> *Student authors with equal contribution* |
|
|
| </div> |
|
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| This repository contains the pre-trained neural loss recovery module checkpoints for the **ReVo** system. These models are designed to perform neural loss recovery for volumetric videoconferencing systems, recovering lost packets across different video codecs. |
|
|
| ## Model Details |
|
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| The repository includes 6 distinct models, organized by the compression codec and the RGB and Depth modality. |
|
|
| **Supported Codecs:** |
| * `h264` (Advanced Video Coding) |
| * `h265` (High Efficiency Video Coding / HEVC) |
| * `dcvcrt` (Deep Contextual Video Compression) |
|
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|
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|
|
| ## Repository Structure |
| ```text |
| umass-lass/ReVo |
| ├── h264/ |
| │ ├── h264_rgb.pth |
| │ ├── h264_depth.pth |
| ├── h265/ |
| │ ├── h265_rgb.pth |
| │ ├── h265_depth.pth |
| └── dcvcrt/ |
| ├── dcvcrt_rgb.pth |
| ├── dcvcrt_depth.pth |
| ``` |
|
|
| # How to Use? |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import torch |
| |
| # Define the specific codec and modality you want to load |
| codec = "h265" # Options: 'h264', 'h265', 'dcvcrt' |
| modality = "depth" # Options: 'rgb', 'depth' |
| |
| # Download the specific checkpoint |
| checkpoint_path = hf_hub_download( |
| repo_id="umass-lass/ReVo", |
| filename=f"{codec}/{codec}_{modality}.pth" |
| ) |
| |
| # Load the weights into your PyTorch model |
| # model.load_state_dict(torch.load(checkpoint_path)) |
| print(f"Successfully downloaded to: {checkpoint_path}") |
| ``` |
|
|
| # Citation |
| (Coming Soon) |
