Sampling Novel Views from a Single 2D Image

Given an input RGB image, we simulate a 3D interactive experience by generating novel viewpoints to account for disocclusion. Builds upon SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting by Jamapani et al., ICCV 2021 (Oral). Unlike SLIDE, we outpaint the input image with a denoising probabilistic diffusion model, use a matting model to separate the background from the foreground image, and construct our meshes with the Open3D library in place of Tensorflow 3D.

Getting Started

• Setup and activate conda environment.

conda create -n sample_novel_views python=3.8
conda activate sample_novel_views

• Install required pre-requisites.

pip install -r requirements.txt

• Add additional .jpg or .png test images to the images folder.
• Run the the code using this command

python main.py --config config.yaml

For each image in the images folder the relevant meshes will be saved to the meshes folder and the output videos will be saved to the outputs folder.

Acknowledgments

Our work builds upon SLIDE: Single Image 3D Photography with Soft Layering and Depth-aware Inpainting by Jamapani et al. ICCV 2021 (Oral). For code structure, we drew inspiration from 3D Photography using Context-aware Layered Depth Inpainting by Shih et al. CVPR 2020. Thank you to the authors of the latter paper for making their code publicly available.

지우 -> Depth anything v2로 estimation하도록 만들기

유진 -> SD쓰지 않고 논문대로 구현

재훈 -> 데이터셋 구하고 eval할수 있도록 환경만들기