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README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+---
+
+# Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer (NeurIPS 2024)
+
+
+## ✨ News
+
+- Feb 11, 2025: 🔨 We are working on the Gradio demo and will release it soon!
+- Feb 11, 2025: 🎁 Enjoy our improved version of Direct3D with high quality geometry and texture at [https://www.neural4d.com](https://www.neural4d.com/).
+- Feb 11, 2025: 🚀 Release inference code of Direct3D and the pretrained models are available at 🤗 [Hugging Face](https://huggingface.co/DreamTechAI/Direct3D/tree/main).
+
+## 📝 Abstract
+
+We introduce **Direct3D**, a native 3D generative model scalable to in-the-wild input images, without requiring a multiview diffusion model or SDS optimization. Our approach comprises two primary components: a Direct 3D Variational Auto-Encoder **(D3D-VAE)** and a Direct 3D Diffusion Transformer **(D3D-DiT)**. D3D-VAE efficiently encodes high-resolution 3D shapes into a compact and continuous latent triplane space. Notably, our method directly supervises the decoded geometry using a semi-continuous surface sampling strategy, diverging from previous methods relying on rendered images as supervision signals. D3D-DiT models the distribution of encoded 3D latents and is specifically designed to fuse positional information from the three feature maps of the triplane latent, enabling a native 3D generative model scalable to large-scale 3D datasets. Additionally, we introduce an innovative image-to-3D generation pipeline incorporating semantic and pixel-level image conditions, allowing the model to produce 3D shapes consistent with the provided conditional image input. Extensive experiments demonstrate the superiority of our large-scale pre-trained Direct3D over previous image-to-3D approaches, achieving significantly better generation quality and generalization ability, thus establishing a new state-of-the-art for 3D content creation.
+
+
+## 🚀 Getting Started
+
+### Installation
+
+```sh
+git clone https://github.com/DreamTechAI/Direct3D.git
+
+cd Direct3D
+
+pip install -r requirements.txt
+
+pip install -e .
+```
+
+### Usage
+
+```python
+from direct3d.pipeline import Direct3dPipeline
+pipeline = Direct3dPipeline.from_pretrained("DreamTechAI/Direct3D")
+pipeline.to("cuda")
+mesh = pipeline(
+    "assets/devil.png",
+    remove_background=False, # set to True if the background of the image needs to be removed
+    mc_threshold=-1.0,
+    guidance_scale=4.0,
+    num_inference_steps=50,
+)["meshes"][0]
+mesh.export("output.obj")
+```
+
+## 🤗 Acknowledgements
+
+Thanks to the following repos for their great work, which helps us a lot in the development of Direct3D:
+
+- [3DShape2VecSet](https://github.com/1zb/3DShape2VecSet/tree/master)
+- [Michelangelo](https://github.com/NeuralCarver/Michelangelo)
+- [Objaverse](https://objaverse.allenai.org/)
+- [diffusers](https://github.com/huggingface/diffusers)
+
+## 📖 Citation
+
+If you find our work useful, please consider citing our paper:
+
+```bibtex
+@article{direct3d,
+  title={Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer},
+  author={Wu, Shuang and Lin, Youtian and Zhang, Feihu and Zeng, Yifei and Xu, Jingxi and Torr, Philip and Cao, Xun and Yao, Yao},
+  journal={arXiv preprint arXiv:2405.14832},
+  year={2024}
+}
+```
+
+---