NFD

This is the official codebase for the paper "3D Neural Field Generation using Triplane Diffusion."

3D Neural Field Generation using Triplane Diffusion
J. Ryan Shue*, Eric Ryan Chan*, Ryan Po*, Zachary Ankner*, Jiajun Wu, and Gordon Wetzstein
* equal contribution

https://jryanshue.com/nfd/

Abstract: Diffusion models have emerged as the state-of-the-art for image generation, among other tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields and factoring them into a set of axis-aligned triplane feature representations. Thus, our 3D training scenes are all represented by 2D feature planes, and we can directly train existing 2D diffusion models on these representations to generate 3D neural fields with high quality and diversity, outperforming alternative approaches to 3D-aware generation. Our approach requires essential modifications to existing triplane factorization pipelines to make the resulting features easy to learn for the diffusion model. We demonstrate state-of-the-art results on 3D generation on several object classes from ShapeNet.

Setup

Run:

cd nfd
conda env create -f environment.yml
conda activate nfd
pip install -e .

Download pretrained models:

source download_models.sh

Sampling from pretrained models

To run the models from our paper:

cd nfd
conda activate nfd

Cars:

python gen_samples.py --ddpm_ckpt models/cars/ddpm_cars_ckpts/ema_0.9999_405000.pt \
    --decoder_ckpt models/cars/car_decoder.pt --stats_dir models/cars/statistics/cars_triplanes_stats \
    --save_dir samples/cars_samples --num_samples 8 --num_steps 250 --shape_resolution 256

Chairs:

python gen_samples.py --ddpm_ckpt models/chairs/ddpm_chairs_ckpts/ema_0.9999_200000.pt \
    --decoder_ckpt models/chairs/chair_decoder.pt --stats_dir models/chairs/statistics/chairs_triplanes_stats \
    --save_dir samples/chairs_samples --num_samples 8 --num_steps 250 --shape_resolution 256

Planes:

python gen_samples.py --ddpm_ckpt models/planes/ddpm_planes_ckpts/ema_0.9999_220000.pt \
    --decoder_ckpt models/planes/plane_decoder.pt --stats_dir models/planes/statistics/planes_triplanes_stats \
    --save_dir samples/planes_samples --num_samples 8 --num_steps 250 --shape_resolution 256

Training

Coming soon!