Spice·E: Structural Priors in 3D Diffusion using Cross-Entity Attention

This is the official pytorch implementation of Spice·E.

[Project Website]

Spice·E: Structural Priors in 3D Diffusion using Cross-Entity Attention
Etai Sella*, Gal Fiebelman*, Noam Atia, Hadar Averbuch-Elor
Tel Aviv University
* Denotes equal contribution

Abstract
We are witnessing rapid progress in automatically generating and manipulating 3D assets due to the availability of pretrained text-to-image diffusion models. However, time-consuming optimization procedures are required for synthesizing each sample, hindering their potential for democratizing 3D content creation. Conversely, 3D diffusion models now train on millionscale 3D datasets, yielding high-quality text-conditional 3D samples within seconds. In this work, we present Spice·E – a neural network that adds structural guidance to 3D diffusion models, extending their usage beyond text-conditional generation. At its core, our framework introduces a crossentity attention mechanism that allows for multiple entities—in particular, paired input and guidance 3D shapes—to interact via their internal representations within the denoising network.We utilize this mechanism for learning task-specific structural priors in 3D diffusion models from auxiliary guidance shapes.We show that our approach supports a variety of applications, including 3D stylization, semantic shape editing and text-conditional abstractionto- 3D, which transforms primitive-based abstractions into highly-expressive shapes. Extensive experiments demonstrate that Spice·E achieves SOTA performance over these tasks while often being considerably faster than alternative methods. Importantly, this is accomplished without tailoring our approach for any specific task. We will release our code and trained models.

Getting Started

Getting the repo

git clone git@github.com:TAU-VAILab/Spice-E.git
cd Spice-E

Setting up environment

conda create --name Spice-E python=3.10 --yes
conda activate Spice-E
pip install -e .
pip install pyyaml
pip install ipywidgets

Setting up blender

1. Get 'blender-3.4.1-linux-x64.tar.xz' from here.
2. Unzip it
3. Set your BLENDER_PATH environment by running:
   export BLENDER_PATH=\<path to your unzipped blender-3.4.1-linux-x64.tar.xz file\>/blender
   

Running The Demo

Download the "spice_e_abstract_to_3d_chair" model from here.
For the demo using an already encoded guidance shape, run:

python3 run_inference.py -m <path to downloaded chair model> -d demo/latent_inference/demo_latent.pt -o outputs -p "funky swivel chair"

For the demo using an guidance shape that is not yet encoded, run:

python3 run_inference.py -m <path to downloaded chair model> -d demo/object_inference/ -o outputs -p "funky swivel chair" --encode_guidance --input_guidance_object_path demo/object_inference/demo_guidance_shape.ply

When finished you should see a 360 rendering video of the output in:

Spice-E/outputs/funky_swivel_chair/output/output.mp4

General Usage

Getting the trained models

All the trained models used in the paper can be downloaded from here

Creating a latent dataset

We show an example script for creating a latent dataset for the 3D Stylization task.
To create a latent dataset for the 3D Stylization task, run:

python3 get_stylization_latents.py -o <path to output folder>

To create a latent dataset for the 3D Stylization task with refined annotations, download the finetuned blip model dir from here and run:

python3 get_stylization_latents.py -o <path to output folder> --use_blip_refinement --blip_model_path <path to downloaded finetuned blip model dir>

For the text-conditional abstraction-to-3D dataset we used code from the [CuboidAbstractionViaSeg](https://github.com/SilenKZYoung/CuboidAbstractionViaSeg) repo and for the semantic shape editing dataset we used code from the [changeit3d](https://github.com/optas/changeit3d) repo.

Training a Spice-E model

We show an example script for training a Spice-E model for the 3D Stylization task. To train a Spice-E model for the 3D Stylization task, run:

python3 train_spice_e.py -d <path to data folder> -o <path to output folder>

Inference

To infer from a trained Spice-E model using latents that were encoded using the Shap-E encoder, run:

python3 run_inference.py -m <path to downloaded chair model> -d <path to the guidance shape latent> -o <path to output folder> -p <text prompt>

To infer from a trained Spice-E model using a 3D guidance shape that has not been encoded using the Shap-E encoder, run:

python3 run_inference.py -m <path to downloaded chair model> -d <path to folder in which to save the encoded guidance shape latent> -o <path to output folder> -p <text prompt> --encode_guidance --input_guidance_object_path <path to input 3D guidance shape>

BibTeX

If you find our work useful in your research, please consider citing:

@article{sella2023spic,
 title={Spice-E : Structural Priors in 3D Diffusion using Cross-Entity Attention},
 author={Etai Sella and Gal Fiebelman and Noam Atia and Hadar Averbuch-Elor},
 year={2024},
 eprint={2311.17834},
 archivePrefix={arXiv},
 primaryClass={cs.CV}
}

Acknowledgements

We thank the authors of Shap-E for their wonderful code on which we base our own.