github.com/Adamdad/hash3D @main

Training-free Acceleration for 3D Generation 🏎️💨

## Introduction This repository contains the official implementation for our paper **Hash3D: Training-free Acceleration for 3D Generation** 🥯[[Project Page](https://adamdad.github.io/hash3D/)] 📝[[Paper](https://arxiv.org/abs/2404.06091)] [[code](https://github.com/Adamdad/hash3D)] Xingyi Yang, Xinchao Wang National University of Singapore  > We present Hash3D, a universal solution to acclerate score distillation samplin (SDS) based 3D generation. By effectively hashing and reusing these feature maps across neighboring timesteps and camera angles, Hash3D substantially prevents redundant calculations, thus accelerating the diffusion model's inference in 3D generation tasks. **What we offer**: - ⭐ Compatiable to Any 3D generation method using SDS. - ⭐ Inplace Accerlation for 1.3X - 4X. - ⭐ Training-Free. ## Results Visualizations

Image-to-3D Results

Input Image	Zero-1-to-3	Hash3D + Zero-1-to-3 $${\color{red} \text{(Speed X4.0)}}$$
	https://github.com/Adamdad/hash3D/assets/26020510/797d78f0-d2d7-43a3-94af-bf57c9c5ef70	https://github.com/Adamdad/hash3D/assets/26020510/c02701f1-fd92-4601-8569-18c7c17cde97
	https://github.com/Adamdad/hash3D/assets/26020510/a41ba688-40bf-4d95-95de-37b669a90887	https://github.com/Adamdad/hash3D/assets/26020510/86d9e46d-0554-4a87-9960-ce3a9f83bdd7

Text-to-3D Results

Prompt	Gaussian-Dreamer	Hash3D + Gaussian-Dreamer $${\color{red}\text{(Speed X1.5)}}$$
A bear dressed as a lumberjack	https://github.com/Adamdad/hash3D/assets/26020510/80a4658f-7233-49aa-a357-ff296396185b	https://github.com/Adamdad/hash3D/assets/26020510/3882341f-c5f1-4f4f-8f24-d1c080ecdb2f
A train engine made out of clay	https://github.com/Adamdad/hash3D/assets/26020510/1111d8ba-aae5-4117-9340-5d950702e49b	https://github.com/Adamdad/hash3D/assets/26020510/06b7bbf3-0edb-4d2f-a2f2-c11bab5c7b64

## Project Structure The repository is organized into three main directories, each catering to a different repo that Hash3D can be applied on: 1. `threesdtudio-hash3d`: Contains the implementation of Hash3D tailored for use with the [`threestudio`](https://github.com/threestudio-project/threestudio). 2. `dreamgaussian-hash3d`: Focuses on integrating Hash3D with the DreamGaussian for image-to-3D generation. 3. `gaussian-dreamer-hash3d`: Dedicated to applying Hash3D to GaussianDreamer for faster text-to-3D tasks. ### What we add? The core implementation is in the `guidance_loss` for each SDS loss computation. We See `hash3D/threestudio-hash3d/threestudio/models/guidance/zero123_unified_guidance_cache.py` for example. The code for the hash table implementation is in `hash3D/threestudio-hash3d/threestudio/utils/hash_table.py`. ## Getting Started ### Installation Navigate to each of the specific directories for environment-specific installation instructions. ### Usage Refer to the `README` within each directory for detailed usage instructions tailored to each environment. For example, to run Zero123+SDS with hash3D

cd threestudio-hash3d
python launch.py --config configs/stable-zero123_hash3d.yaml --train --gpu 0 data.image_path=https://adamdad.github.io/hash3D/load/images/dog1_rgba.png

### Evaliation 1. **Image-to-3D**: GSO dataset GT meshes and renderings can be found online. With the rendering of the reconstructed 3D objects at `pred_dir` and the gt rendering at `gt_dir`, run

python eval_nvs.py --gt $gt_dir --pr $pred_dir 

2. **Text-to-3D**: Run all the prompts in `assets/prompt.txt`. And compute the CLIP score between text and rendered image as

python eval_clip_sim.py "$gt_prompt" $pred_dir --mode text

## Acknowledgement We borrow part of the code from [DeepCache](https://github.com/horseee/DeepCache) for feature extraction from diffusion models. We also thanks the implementation from [threestudio](https://github.com/threestudio-project/threestudio), [DreamGaussian](https://github.com/dreamgaussian/dreamgaussian), [Gaussian-Dreamer](https://github.com/hustvl/GaussianDreamer), and the valuable disscussion with [@FlorinShum](https://github.com/FlorinShum) and [@Horseee](https://github.com/horseee). ## Citation

@misc{yang2024hash3d,
      title={Hash3D: Training-free Acceleration for 3D Generation}, 
      author={Xingyi Yang and Xinchao Wang},
      year={2024},
      eprint={2404.06091},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}