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umass-lass/
ReVo

Name: ReVo
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ReVo: A Cross-Layer Reliable Volumetric Videoconferencing System

Project Website

Ankur Aditya^1*, Diptyaroop Maji^1*, Lingdong Wang¹, Bhavya Ramakrishna², Ramesh Sitaraman^1,3, Prashant Shenoy¹

¹University of Massachusetts Amherst ²Dolby Labs ³Akamai Tech
^* Student authors with equal contribution

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

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