4D-fy - threestudio

| Project Page | Paper |

• This code is forked from threestudio.

Installation

Install threestudio

This part is the same as original threestudio. Skip it if you already have installed the environment.

See installation.md for additional information, including installation via Docker.

• You must have an NVIDIA graphics card with at least 40GB VRAM and have CUDA installed.
• Install Python >= 3.8.
• (Optional, Recommended) Create a virtual environment:

python3 -m virtualenv venv
. venv/bin/activate

# Newer pip versions, e.g. pip-23.x, can be much faster than old versions, e.g. pip-20.x.
# For instance, it caches the wheels of git packages to avoid unnecessarily rebuilding them later.
python3 -m pip install --upgrade pip

• Install PyTorch >= 1.12. We have tested on torch1.12.1+cu113 and torch2.0.0+cu118, but other versions should also work fine.

# torch1.12.1+cu113
pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 --extra-index-url https://download.pytorch.org/whl/cu113
# or torch2.0.0+cu118
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118

• (Optional, Recommended) Install ninja to speed up the compilation of CUDA extensions:

pip install ninja

• Install dependencies:

pip install -r requirements.txt

Install MVDream

MVDream multi-view diffusion model is provided in a different codebase. Install it by:

git clone https://github.com/bytedance/MVDream extern/MVDream
pip install -e extern/MVDream 

Quickstart

Our model is trained in 3 stages and there are three different config files for every stage. Training has to be resumed after finishing a stage.

seed=0
gpu=0

# Stage 1
# python launch.py --config configs/fourdfy_stage_1.yaml --train --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a dog riding a skateboard"

# Stage 2
# ckpt=/path/to/fourdfy_stage_1/a_dog_riding_a_skateboard@timestamp/ckpts/last.ckpt
# python launch.py --config configs/fourdfy_stage_2.yaml --train --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a dog riding a skateboard" system.weights=$ckpt

# Stage 3
# ckpt=/path/to/fourdfy_stage_2/a_dog_riding_a_skateboard@timestamp/ckpts/last.ckpt
# python launch.py --config configs/fourdfy_stage_3.yaml --train --gpu $gpu exp_root_dir=$exp_root_dir seed=$seed system.prompt_processor.prompt="a dog riding a skateboard" system.weights=$ckpt

Tips

• Memory Usage. Depending on the text prompt, stage 3 might not fit on a 40/48 GB GPU. An easy way to reduce memory usage is to reduce the number of ray samples with system.renderer.num_samples_per_ray=256 or system.renderer.num_samples_per_ray=128. Another way is to reduce the rendering resolution for the video model with data.single_view.width_vid=144 and data.single_view.height_vid=80.
• More motion. To increase the motion, the learning rate for the video model can be increased to system.loss.lambda_sds_video=0.3 or system.loss.lambda_sds_video=0.5.

Credits

This code is built on the threestudio-project and MVDream-threestudio. Thanks to the maintainers for their contribution to the community!

Citing

If you find 4D-fy helpful, please consider citing:

@article{bah20234dfy,
  author = {Bahmani, Sherwin and Skorokhodov, Ivan and Rong, Victor and Wetzstein, Gordon and Guibas, Leonidas and Wonka, Peter and Tulyakov, Sergey and Park, Jeong Joon and Tagliasacchi, Andrea and Lindell, David B.},
  title = {4D-fy: Text-to-4D Generation Using Hybrid Score Distillation Sampling},
  journal = {arXiv},
  year = {2023},
}