README.md

---
license: mit
tags:
- computer-vision
- pytorch
- volumetric-video
- multimedia-systems 
---

<div align="center">

# ReVo: A Cross-Layer Reliable Volumetric Videoconferencing System

[![Project Website](https://img.shields.io/badge/Project-Website-blue.svg)](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 &nbsp;&nbsp;&nbsp; <sup>2</sup>Dolby Labs &nbsp;&nbsp;&nbsp; <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)