metadata
license: other
license_name: sculpt4d
license_link: https://github.com/TencentARC/Sculpt4D/blob/main/LICENSE.txt
library_name: pytorch
pipeline_tag: image-to-3d
tags:
- 4d-generation
- 3d-generation
- diffusion-transformer
- mesh-generation
- sculpt4d
Sculpt4D
Pretrained model for Sculpt4D: Generating 4D Shapes via Sparse-Attention Diffusion Transformers.
Given an image sequence of an animated object, Sculpt4D generates a temporally coherent sequence of 3D meshes. It integrates efficient temporal modeling into a pretrained 3D Diffusion Transformer (Hunyuan3D-2.1) via a Block Sparse Attention mechanism.
- 🌐 Project page: https://visual-ai.github.io/sculpt4d/
- 📄 arXiv: https://arxiv.org/abs/2604.21592
- 💻 Code: https://github.com/TencentARC/Sculpt4D
Checkpoint
This repository hosts the bf16 checkpoint (~8 GB), under the blockmask_bf16/ subfolder:
blockmask_bf16/
├── pytorch_model-00001-of-00002.bin
├── pytorch_model-00002-of-00002.bin
└── pytorch_model.bin.index.json
Usage
Download the checkpoint:
huggingface-cli download TencentARC/Sculpt4D --include "blockmask_bf16/*" --local-dir checkpoints/sculpt4d
Run inference (see the code repository for full setup):
python inference_4d.py \
--config configs/4d_config_8.yaml \
--ckpt_path checkpoints/sculpt4d/blockmask_bf16 \
--input_dir demos/door \
--output_dir ./inference_output/door
Citation
@inproceedings{sculpt4d2026,
title={Sculpt4D: Generating 4D Shapes via Sparse-Attention Diffusion Transformers},
author={Yin, Minghao and Hu, Wenbo and Xu, Jiale and Shan, Ying and Han, Kai},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2026}
}