---
license: mit
tags:
  - video-compression
  - implicit-neural-representations
  - hypernetwork
  - pytorch
---

# TeCoNeRV Model Checkpoints

TeCoNeRV uses hypernetworks to predict implicit neural representation (INR) weights for video compression. A patch-tubelet decomposition enables hypernetworks to scale to high-resolution video prediction. The temporal coherence objective reduces redundancy across consecutive clips, enabling compact residual encoding of per-clip parameters.

This repository contains hypernetwork training checkpoints for the three model families described in the paper.

## Model families

`nervenc` — Baseline NeRVEnc hypernetwork. Predicts full-resolution clip reconstructions directly.

`patch_tubelet` — Proposed patch-tubelet hypernetwork. Predicts parameters for spatial tubelets; full frames are reconstructed by tiling. Supports resolution-independent inference.

`teconerv` — Proposed method. Initialized from a `patch_tubelet` checkpoint and finetuned with a temporal coherence objective.

## Getting started

See the [GitHub repository](https://github.com/namithap10/TeCoNeRV) for full documentation on setup, training, and evaluation. Checkpoint download instructions are in `docs/models.md`.

```bash
git lfs install
git clone https://huggingface.co/namithap/teconerv-models
```

## Citation

```bibtex
@article{padmanabhan2026teconerv,
  title={TeCoNeRV: Leveraging Temporal Coherence for Compressible Neural Representations for Videos},
  author={Padmanabhan, Namitha and Gwilliam, Matthew and Shrivastava, Abhinav},
  journal={arXiv preprint arXiv:2602.16711},
  year={2026}
}
```