Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion

Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion
Linzhan Mou, Jun-Kun Chen, Yu-Xiong Wang
CVPR 2024

🧷 News

• [2024-09-22] The single-view setting codebase is released.
• [2024-08-29] The multi-view setting codebase is released.

🔧 Installation

Environmental Setups

git clone https://github.com/Friedrich-M/Instruct-4D-to-4D.git
cd Instruct-4D-to-4D
conda create -n instruct4d python=3.8
conda activate instruct4d
pip install torch==2.0.1+cu118 torchvision==0.15.2+cu118 --index-url https://download.pytorch.org/whl/cu118
pip install -r requirements.txt
# For single-view setting (NeRFStudio) also install the following packages; for more details, please refer to the [NeRFStudio](https://github.com/nerfstudio-project/nerfstudio) repository.
pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install -e nerfplayer-nerfstudio

Data Preparation

For multi-view 4D scenes. You can download scenes from DyNeRF Dataset.

mkdir -p data/neural_3d && cd data/neural_3d
# take coffee_martini as an example
wget https://github.com/facebookresearch/Neural_3D_Video/releases/download/v1.0/coffee_martini.zip && unzip coffee_martini.zip && cd ../..
# run the following command to generate the images
python tools/prepare_video.py data/neural_3d/coffee_martini

For single-view 4D scenes. You can download scenes from DyCheck Dataset.

🚀 Training

Multi-View Setting

To edit a 4D scene, you must first train a regular 4D NeRF using your data. For example:

python stream_train.py --config configs/n3dv/train_coffee_50_2.txt \
    --datadir ./data/neural_3d/coffee_martini \
    --basedir ./log/neural_3d \
    --render_test 1 --render_path 1  

Once you have fully trained your scene, the checkpoints will be saved to the log directory. We also provide some checkpoints of pre-trained 4D NeRF here. You can download and put them into the log directory. The filename should be like train_{scene_name}_{num_frames}_{downsample_factor}.

To start training for editing the NeRF, run the following command:

python stream_edit.py --config configs/n3dv/edit_coffee_50_2.txt \
    --datadir data/neural_3d/coffee_martini \
    --basedir log/neural_3d --expname edit_coffee_50_2 \
    --ckpt log/neural_3d/train_coffee_50_2/ckpt-99999.th \
    --prompt 'What if it was painted by Van Gogh?' \
    --guidance_scale 9.5 --image_guidance_scale 1.5 \
    --diffusion_steps 20 --refine_num_steps 600 --refine_diffusion_steps 4 \
    --restview_refine_num_steps 700 --restview_refine_diffusion_steps 6

To render the trained or edited results, please follow the instructions below:

python stream_train.py --render_only 1 \
    --config configs/n3dv/train_coffee_50_2.txt \
    --datadir data/neural_3d/coffee_martini \
    --ckpt log/neural_3d/train_coffee_50_2/ckpt-99999.th \
    --basedir log/neural_3d \
    --render_test 1 --render_path 1 

Single-View Setting

We adopt the NeRFStudio version of NeRFPlayer to train the single-view 4D NeRF. Please dive into the nerfplayer-nerfstudio directory and follow the instructions below to edit the single-view 4D NeRF.

Training the single-view 4D NeRF is similar to the multi-view setting. You can run the following command:

sh run_nerf.sh

We also provide some checkpoints of pre-trained 4D NeRF here.

To start training for editing the NeRF, run the following command:

sh run_edit.sh

To render the edited 4D scene, you can run the following command:

sh run_render.sh

To compare with the baseline Instruct NeRF2NeRF, you can run the following command:

sh run_in2n.sh

4D Editing tips:

• Since we use the Parallelization scheme, please make sure you have at least 2 GPUs available.
• If you encounter the CUDA OOM issue, please try to reduce the sequence length of Anchor-Aware IP2P.
• You can run the nerfplayer-nerfstudio/instruct-pix2pix/edit_app.py to edit the 2D image onlinely.

If you have any other questions, please feel free to open an issue or e-mail at moulz@zju.edu.cn.

📜 TODO List

• Release the codebase of single-view setting (within one week)
• Replace the NeRF backbone with 4D GS
• Provide the pipeline of original version (key-view editing -> pseudo-view propagation)

🔥 Framework

We provide some demos below for better understanding our framework components.

Please dive into the ip2p_models directory and download the example files from Google Drive.

gdown 1aNwZ4prQk6z1DJtIg9ssNroTbBK6YLnK

(1) Anchor-Aware Instruct-Pix2Pix (IP2P)

To enable InsturctPix2Pix simultaneously edit multiple frames and achieve within-batch consistency, we modify the attention and convolution structure of original IP2P.

# Single IP2P
python test_ip2p.py --image_path ./examples/coffee_frame_2x/0.png \
    --prompt 'What if it was painted by Van Gogh?' \
    --resize 1024 --steps 20 \
    --guidance_scale 10.5 --image_guidance_scale 1.5
# Anchor-Aware IP2P
python test_ip2p_sequence.py --image_dir ./examples/coffee_frame_2x/ \
    --prompt 'What if it was painted by Van Gogh?' \
    --sequence_length 6 --resize 1024 --steps 20 \
    --guidance_scale 10.5 --image_guidance_scale 1.5

(2) Key Pseudo-View Editing (Temporal Consistency)

Along the temporal dimension, to achieve cross-batch consistency in long-term sequence editing, we propose flow-guided sliding window warping, with anchor-aware IP2P painting.

# Flow-Guided Warping
python test_flow.py \
    --source_img ./examples/coffee_frame_2x/3.png \
    --target_img ./examples/coffee_frame_2x/6.png
# Sliding Window Warping w/ Anchor-Aware IP2P Painting
python test_flow_sequence.py \
    --image_dir ./examples/coffee_frame_2x/ \
    --prompt 'What if it was painted by Van Gogh?' \
    --sequence_length 6 --resize 1024 \
    --guidance_scale 10.5 --image_guidance_scale 1.5 \
    --painting_diffusion_steps 5 --painting_num_train_timesteps 600

(3) Pseudo-View Propagation (Spatial Consistency)

According to the principal of Perspective Transformation, we could use rendered depth from 4D NeRF with the camera parameters to warp the edited pseudo-view to the target view, while maintaining spatial consistency.

# Depth-Based Warping
python test_depth.py \
    --source_img ./examples/coffee_cam_2x/0.png \
    --target_img ./examples/coffee_cam_2x/1.png \
    --prompt 'What if it was painted by Van Gogh?' \
    --guidance_scale 10.5 --image_guidance_scale 1.5 \
    --pts_path ./examples/pts_0.pt --warp_path ./examples/warp_0.pt

📂 Notes

[1] 2D Editing Quality. If your edit isn't working as you desire, it is likely because InstructPix2Pix struggles with your images and prompt. We recommend taking one of your images and trying to edit it in 2D first with InstructPix2Pix, referring to the tips on getting a good edit can be found here.

python test_ip2p.py --image_path $IMAGE_PATH --prompt $PROMPT

[2] 4D Scene Representation. Our framework is general, and therefore, any 4D scene representation adopting RGB observations as supervision can be used. We encourage to extend our editing pipeline to 4D Gaussian Splatting to make the editing more efficient.

Acknowledgement

We would like to thank Liangchen Song for providing the codebase of NeRFPlayer and helpful discussion. We also sincerely thank Haque, Ayaan for kind discussion about 3D scene editing.

📝 Citation

You can find our paper on arXiv.

If you find this code or find the paper useful for your research, please consider citing:

@inproceedings{mou2024instruct,
  title={Instruct 4D-to-4D: Editing 4D Scenes as Pseudo-3D Scenes Using 2D Diffusion},
  author={Mou, Linzhan and Chen, Jun-Kun and Wang, Yu-Xiong},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20176--20185},
  year={2024}
}