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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".

TipoUNetModelo basestabilityai/stable-video-diffusion-img2vid-xtVersãoFP16Bnota

Fonte do modelo

Tipo
UNet
Tamanho do arquivo
2,84 GB
Modelo base
stabilityai/stable-video-diffusion-img2vid-xt
Versão
vFP16
Atualizado
1 de nov. de 2026
Downloads
78
Fonte
Hugging Face

Descrição da fonte

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}, 
}

Fontes

1 fonte
Hugging FacePrimárioVersão 8ed51301cb97ec7a61a94a1bff1f7a107f73e9e8
huggingface.co/jhshao/ChronoDepth-v1/tree/8ed51301cb97ec7a61a94a1bff1f7a107f73e9e8

Verificado 1 de nov.

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Repository files

File inventory only; completeness is unverified.

Artefatos de modelo

1 artefato

diffusion_pytorch_model.safetensors

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

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Trechos de fonte

3 trechos

--- 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