Upload meta_xl_tot.csv

.gitattributes

@@ -55,12 +55,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
 meta_xl_animation_tot.csv filter=lfs diff=lfs merge=lfs -text
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README.md

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----
-license: apache-2.0
-task_categories:
-- text-to-3d
-- image-to-3d
-language:
-- en
-tags:
-- 4d
-- 3d
-- text-to-4d
-- image-to-4d
-- 3d-to-4d
-size_categories:
-- 1M<n<10M
----
-
-# Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
-
-[[Project Page]](https://vita-group.github.io/Diffusion4D/) | [[Arxiv]](https://arxiv.org/abs/2405.16645) | [[Code]](https://github.com/VITA-Group/Diffusion4D)
-
-## News
-- 2024.6.28:  Released rendered data from curated [objaverse-xl](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverseXL_curated).
-- 2024.6.4:  Released rendered data from curated [objaverse-1.0](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverse1.0_curated), including orbital videos of dynamic 3D, orbital videos of static 3D, and monocular videos from front view.
-- 2024.5.27: Released metadata for objects!
-
-## Overview
-We collect a large-scale, high-quality dynamic 3D(4D) dataset sourced from the vast 3D data corpus of [Objaverse-1.0](https://objaverse.allenai.org/objaverse-1.0/) and [Objaverse-XL](https://github.com/allenai/objaverse-xl). We apply a series of empirical rules to filter the dataset. You can find more details in our paper. In this part, we will release the selected 4D assets, including:
-1. Selected high-quality 4D object ID.
-2. A render script using Blender, providing optional settings to render your personalized data.
-3. Rendered 4D images by our team to save your GPU time. With 8 GPUs and a total of 16 threads, it took 5.5 days to render the curated objaverse-1.0 dataset.
-
-## 4D Dataset ID/Metadata
-We collect 365k dynamic 3D assets from Objaverse-1.0 (42k) and Objaverse-xl (323k). Then we curate a high-quality subset to train our models. 
-
-Metadata of animated objects (323k) from objaverse-xl can be found in [meta_xl_animation_tot.csv](https://huggingface.co/datasets/hw-liang/Diffusion4D/blob/main/meta_xl_animation_tot.csv). 
-We also release the metadata of all successfully rendered objects from objaverse-xl's Github subset in [meta_xl_tot.csv](https://huggingface.co/datasets/hw-liang/Diffusion4D/blob/main/meta_xl_tot.csv).
-
-For text-to-4D generation, the captions are obtained from the work [Cap3D](https://huggingface.co/datasets/tiange/Cap3D).
-
-
-## Citation
-
-If you find this repository/work/dataset helpful in your research, please consider citing the paper and starring the [repo](https://github.com/VITA-Group/Diffusion4D) ⭐.
-
-```
-@article{liang2024diffusion4d,
-  title={Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models},
-  author={Liang, Hanwen and Yin, Yuyang and Xu, Dejia and Liang, Hanxue and Wang, Zhangyang and Plataniotis, Konstantinos N and Zhao, Yao and Wei, Yunchao},
-  journal={arXiv preprint arXiv:2405.16645},
-  year={2024}
-}
-```
+---
+license: apache-2.0
+---

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