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[NeurIPS 2024] Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models
| Date | Stars |
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| 2026-07-31 | 344 |
| 2026-08-06 | 345 |
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# Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models The official implementation of work "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) | [[Video (Youtube)]](https://www.youtube.com/watch?v=XJT-cMt_xVo) | [[视频 (Bilibili)]](https://b23.tv/ojVe6Uv) | [[Huggingface Dataset]](https://huggingface.co/datasets/hw-liang/Diffusion4D) # Image-to-4D    # Text-to-4D  # 3D-to-4D   # News - 2024.6.28: Released rendered data from curated [objaverse-xl](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverseXL_curated), including orbital videos of dynamic 3D and monocular videos from front view. - 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 and data preparation code! - 2024.5.26: Released on arxiv! # 4D Dataset Preparation  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 curate the source dataset. You can find more details in our [paper](https://arxiv.org/abs/2405.16645). In this part, we will release the selected 4D assets, including: 1. Curated high-quality 4D object ID. 2. A render script using Blender, providing optional settings to render your personalized data. 3. [Rendered objaverse-1.0 4D images](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverse1.0_curated) and [Rendered objaverse-xl 4D images](https://huggingface.co/datasets/hw-liang/Diffusion4D/tree/main/objaverseXL_curated) by our team to save you GPU time. With 8 GPUs and a total of 16 threads, it took **5.5 days** to render the curated objaverse-1.0 dataset and about **30 days** for objaverse-xl dataset. ### 4D Dataset ID/Metadata We first 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. The uncurated 42k IDs of all the animated objects from objaverse-1.0 are in `rendering/src/ObjV1_all_animated.txt`. The curated ~11k IDs of the animated objects from objaverse-1.0 are in `rendering/src/ObjV1_curated.txt`. The curated ~71k IDs of the animated objects from objaverse-xl are in [huggingface](https://huggingface.co/datasets/hw-liang/Diffusion4D/blob/main/objaverseXL_curated/objaverseXL_curated_uuid_list.txt). Metadata of animated objects (323k) from objaverse-xl can be found in [huggingface](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](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). ### 4D Dataset Rendering Script 1. Clone the repository and enter the rendering directory: ```bash git clone https://github.com/VI
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matched fp:ed8aef2ddad3f03c, llm:Repository topics and description: '4d, generative-ai, image-to-4d, text-to-4d' and title/description 'Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models' (NeurIPS 2024).
matched fp:ed8aef2ddad3f03c, llm:Repository topics and description: '4d, generative-ai, image-to-4d, text-to-4d' and title/description 'Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models' (NeurIPS 2024).
matched fp:ed8aef2ddad3f03c, llm:Repository topics and description: '4d, generative-ai, image-to-4d, text-to-4d' and title/description 'Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models' (NeurIPS 2024).