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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>
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
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)
## 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) |