---
license: other
license_name: sample-code-license
license_link: LICENSE
tags:
- egocentric
- 4d-reconstruction
- depth-estimation
- camera-pose-estimation
- gaze-estimation
- human-pose-estimation
- hand-pose-estimation
---

# ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video

**ECCV 2026** · [Code](https://github.com/lvsean/reviv4d) · [Paper](https://arxiv.org/abs/2607.17790) · [Website](https://reviv4d.github.io/)

ReViV reconstructs egocentric 4D from a single monocular RGB video in one feed-forward pass. It recovers the **viewer**, meaning the body and hand motion of the camera wearer, and the **view**, meaning depth, camera trajectory and gaze.

## Checkpoints

This repository holds two self-contained inference sets, one per subfolder. Each set contains the main model (`reviv_main.pth`), the five detokenizers (`reviv_tok_{body,cam,gaze,lhand,rhand}.pth`) and a `norm_stats/` folder with the matching mean/std files. Use the detokenizers and norm stats from the same set as the main model.

| Set | Input | Predicts |
|---|---|---|
| `metric_depth/` | 32-frame 512×512 clip @ 16 fps (Cosmos DV8x16x16) | metric 512×512 depth, camera, gaze, body, hands |
| `reviv_500b/` | 16-frame 256×256 clip @ 8 fps (Cosmos DV4x8x8) + raw clip | relative 256×256 depth, camera, gaze, body, hands |

Depth is decoded with the NVIDIA Cosmos tokenizer, which you download separately (see the [GitHub README](https://github.com/lvsean/reviv4d#inference)).

## Usage

The ReViV environment (`environment.yaml`) pins `huggingface_hub==0.25.2`, which provides `huggingface-cli`:

```bash
huggingface-cli download SeanLyu/reviv --include "metric_depth/*" --local-dir reviv_checkpoints
export REVIV_CKPT_ROOT=reviv_checkpoints/metric_depth
```

Do not upgrade `huggingface_hub` inside the ReViV environment, because diffusers 0.20 requires a version below 0.26. Outside that environment, a newer `huggingface_hub` offers `hf download` with the same arguments.

Then run the demos as described in the [GitHub repository](https://github.com/lvsean/reviv4d#inference). To use the 256 pathway, replace `metric_depth` with `reviv_500b`.

## License

The model weights are released under the Sample Code License (see [LICENSE](LICENSE)), which permits non-commercial use only. The code on GitHub is released under Apache 2.0.

The weights were derived from the following datasets: [Ego-Exo4D](https://ego-exo4d-data.org/), [HoloAssist](https://holoassist.github.io/), [HOT3D](https://facebookresearch.github.io/hot3d/) (Aria and Quest), [ARCTIC](https://arctic.is.tue.mpg.de/), [TACO](https://taco2024.github.io/), [H2O](https://taeinkwon.com/projects/h2o/), [EgoGen](https://ego-gen.github.io/) and [Nymeria](https://www.projectaria.com/datasets/nymeria/). Depth supervision consists of pseudo-labels generated with [Video Depth Anything](https://github.com/DepthAnything/Video-Depth-Anything). This repository does not redistribute any dataset or derived annotation. Each dataset and model listed above has its own license and terms of use, which apply in addition to the license granted here.

Use of the model weights is limited to non-commercial research purposes.

## Citation

```bibtex
@inproceedings{lyu2026reviv,
  title     = {ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video},
  author    = {Lyu, Xiaozhong and Li, Gen and Qian, Zhiyin and Zhang, Xucong and Pollefeys, Marc and Tang, Siyu},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year      = {2026}
}
```