3D-aware Blending with Generative NeRFs
Hyunsu Kim1, Gayoung Lee1, Yunjey Choi1, Jin-Hwa Kim1,2, Jun-Yan Zhu3
1NAVER AI Lab, 2SNU AIIS, 3CMU
Abstract: Image blending aims to combine multiple images seamlessly. It remains challenging for existing 2D-based methods, especially when input images are misaligned due to differences in 3D camera poses and object shapes. To tackle these issues, we propose a 3D-aware blending method using generative Neural Radiance Fields (NeRF), including two key components: 3D-aware alignment and 3D-aware blending.
For 3D-aware alignment, we first estimate the camera pose of the reference image with respect to generative NeRFs and then perform pose alignment for objects. To further leverage 3D information of the generative NeRF, we propose 3D-aware blending that utilizes volume density and blends on the NeRF's latent space, rather than raw pixel space.
For all the experiments, we use a single A100 GPU.
git clone https://github.com/naver-ai/BlendNeRF.git
cd BlendNeRF/eg3dconda env create -f environment.yml
conda activate blendnerf
./install.sh./download.shPretrained EG3D generators:
-
original_ffhq_512-128_400kimg.pklfor FFHQ. As EG3D uses a different crop version of FFHQ compared to the original FFHQ, we fine-tune the EG3D using the original crop version of FFHQ. -
afhqcats512-128.pklfor AFHQv2-Cat.
Camera pose estimators (encoders):
encoder_ffhq.pklfor FFHQ.encoder_afhq.pklfor AFHQv2-Cat.
Semantic segmentation networks. We create target masks for editing to automatically simulate user mask inputs using the networks:
bisenet.ckptfor human faces.deeplab_epoch_19.pthfor cat faces.
Command examples in AFHQ dataset.
# For a single image, the first stage (W optimization) takes 23.6s, and the second stage (G optimization) takes 20.4s on a single A100 GPU.
python inversion.py --outdir=results/inversion --network=checkpoints/afhqcats512-128.pkl --encoder_network checkpoints/encoder_afhq.pkl --original_img_path test_images/afhq/original.png --reference_img_path test_images/afhq/reference.png
- Ours only
# It takes 27 seconds
python blend.py --outdir=results/editing/face --network=checkpoints/afhqcats512-128.pkl --editing_target face --inversion_path=results/inversion --enable_warping True --poisson=False --shapes=True --n_iterations=200 --ref_color_lambda 5 - Ours + Poisson blending
# It takes 12 seconds
python blend.py --outdir=results/editing/face --network=checkpoints/afhqcats512-128.pkl --editing_target face --inversion_path=results/inversion --enable_warping True --poisson=True --shapes=False --n_iterations=100 --ref_color_lambda 5 Argument instructions
--outdir: saving path
--network: pretrained generative NeRF path
--editing_target: blending region (e.g, face) # You can give your custom mask instead of using segmentation networks
--enable_warping: apply local alignment (Appendix C)
--poisson: apply Poisson blending with our method
--shapes: save meshes in .mrc format
--n_iterations: the number of iteration of optimization
--ref_color_lambda: the weight of the image blending loss # setting it zero means we only use the density blending lossBelow command will generate blending results in various settings with test_images/original.png and test_images/reference.png.
python run.py
# Results are in
- results/celeba_hq/inversion
- results/celeba_hq/editing
- results/afhq/inversion
- results/afhq/editingDatasets:
- CelebA-HQ
- AFHQ
Target parts:
- CelebA-HQ (face, hair, nose, lip, eyes)
- AFHQ (face, ears, eyes).
Note that you can give any aribitray mask instead of using pretrained semantic segmentation networks.
Methods:
- Ours
- Ours + Poisson Blending
In our method without Poisson blending, multi-view blending and mesh results are included. Visualize the .mrc mesh file with UCSF Chimerax.
Below command will generate multi-view blending results with diverse input images.
python run_more_images.py
# Results are in
- results/celeba_hq/83_126
- results/celeba_hq/102_314
- ...
- results/afhq/2_325
- results/afhq/171_322
- ...
The model code starts from EG3D. In addition, for the simple user interface, we automatically obtain mask inputs by utilizing the following projects:
However, users are not limited to these options and can use their own custom masks if desired.
Currently, we use PTI for our inversion method. However, we recognize that the inversion process is the bottleneck of our blending pipeline in terms of speed and quality. PTI is slow and sometimes generates inadequate 3D shapes. Additionally, our camera pose estimator (encoder) might not accurate enough to do precise inversion.
If you seek a faster or more accurate inversion method, we suggest exploring other recent 3D-aware inversion approaches, such as:
We use the Iterative Closest Point (ICP) algorithm for local alignment (Appendix C) in AFHQ. However, it may fall into the local extremum, which can impact the alignment quality. For better local alignment, we recommend considering recent pairwise registration techniques.
The source code, pre-trained models, and dataset are available under NVIDIA Source Code License for EG3D.
For technical and other inquires, please contact hyunsu1125.kim@navercorp.com.
If you find this work useful for your research, please cite our paper:
@inproceedings{kim20233daware,
title={3D-aware Blending with Generative NeRFs},
author={Hyunsu Kim and Gayoung Lee and Yunjey Choi and Jin-Hwa Kim and Jun-Yan Zhu},
booktitle={ICCV},
year={2023}
}
We would like to thank Seohui Jeong, Che-Sang Park, Eric R. Chan, Junho Kim, Jung-Woo Ha, Youngjung Uh, and other NAVER AI Lab researchers for their helpful comments and sharing of materials. All experiments were conducted on NAVER Smart Machine Learning (NSML) platform.