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ChronoDepth v1

This model represents the official checkpoint of the paper titled "Learning Temporally Consistent Video Depth from Video Diffusion Priors".

TypeUNetModèle de basestabilityai/stable-video-diffusion-img2vid-xtVersionFP16BClasse

Source du modèle

Type
UNet
Taille du fichier
2,84 GB
Modèle de base
stabilityai/stable-video-diffusion-img2vid-xt
Version
vFP16
Mis à jour
1 nov. 2026
Téléchargements
78
Source
Hugging Face

Description de la source

ChronoDepth: Learning Temporally Consistent Video Depth from Video Diffusion Priors

This model represents the official checkpoint of the paper titled "Learning Temporally Consistent Video Depth from Video Diffusion Priors".

Website PaperGitHub

Jiahao Shao*, Yuanbo Yang*, Hongyu Zhou, Youmin Zhang, Yujun Shen, Vitor Guizilini, Yue Wang, Matteo Poggi, Yiyi Liao†

🎓 Citation

Please cite our paper if you find this repository useful:

@misc{shao2024learningtemporallyconsistentvideo,
      title={Learning Temporally Consistent Video Depth from Video Diffusion Priors}, 
      author={Jiahao Shao and Yuanbo Yang and Hongyu Zhou and Youmin Zhang and Yujun Shen and Vitor Guizilini and Yue Wang and Matteo Poggi and Yiyi Liao},
      year={2024},
      eprint={2406.01493},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2406.01493}, 
}

Sources

1 source
Hugging FacePrincipalVersion 8ed51301cb97ec7a61a94a1bff1f7a107f73e9e8
huggingface.co/jhshao/ChronoDepth-v1/tree/8ed51301cb97ec7a61a94a1bff1f7a107f73e9e8

Vérifié 1 nov.

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File inventory only; completeness is unverified.

Artefacts du modèle

1 artefact

diffusion_pytorch_model.safetensors

safetensors · FP16 (unverified) · 2,84 GB · SHA-256 9b5db40028bf…b469 · Hugging Face

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Extraits de sources

3 extraits

--- license: mit library_name: diffusers pipeline_tag: depth-estimation tags: - video depth estimation base_model: - stabilityai/stable-video-diffusion-img2vid-xt --- # ChronoDepth: Learning Temporally Consistent Video Depth from Video Diffusion Priors This model represents the official checkpoint of the paper titled "Learning Temporally Consistent Video Depth from Video Diffusion Priors". [![Website](https://img.shields.io/website?url=https%3A%2F%2Fjhaoshao.github.io%2FChronoDepth%2F&up_message=ChronoDepth&up_color=blue&style=flat&logo=timescale&logoColor=%23FFDC0F)](https://xdimlab.github.io/ChronoDepth/) [![Paper](https://img.shields.io/badge/arXiv-PDF-b31b1b)](https://arxiv.org/abs/2406.01493)[![GitHub](https://img.shields.io/github/stars/jhaoshao/ChronoDepth?style=default&label=GitHub%20★&logo=github)](https://github.com/jhaoshao/ChronoDepth) [Jiahao Shao*](https://jiahao-shao1.github.io/), Yuanbo Yang*, Hongyu Zhou, [Youmin Zhang](https://youmi-zym.github.io/), [Yujun Shen](https://shenyujun.github.io/), [Vitor Guizilini](https://vitorguizilini.github.io/), [Yue Wang](https://yuewang.xyz/), [Matteo Poggi](https://mattpoggi.github.io/), [Yiyi Liao†](https://yiyiliao.github.io/ ) ## 🎓 Citation Please cite our paper if you find this repository useful: ```bibtex @misc{shao2024learningtemporallyconsistentvideo, title={Learning Temporally Consistent Video Depth from Video Diffusion Priors}, author={Jiahao Shao and Yuanbo Yang and Hongyu Zhou and Youmin Zhang and Yujun Shen and Vitor Guizilini and Yue Wang and Matteo Poggi and Yiyi Liao}, year={2024}, eprint={2406.01493}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2406.01493}, } ```

Source context: 78 downloads · 1 likes · Pipeline depth-estimation · Library diffusers · Repo jhshao/ChronoDepth-v1

Source context: 20 downloads · 1 likes · Pipeline depth-estimation · Library diffusers · Repo jhshao/ChronoDepth-v1