SAIR ECCV2024

We propose the semantic-aware implicit representation by learning semantic-aware implicit representation (SAIR), that is, we make the implicit representation of each pixel rely on both its appearance and semantic information (e.g., which object does the pixel belong to). This work is publised in ECCV 2024.

Environment

• Python 3
• Pytorch 1.6.0
• TensorboardX
• yaml, numpy, tqdm, imageio

Data

mkdir load for putting the dataset folders.

• celebAHQ: mkdir load/celebAHQ and cp scripts/resize.py load/celebAHQ/, then cd load/celebAHQ/. Download and unzip data1024x1024.zip from the Google Drive link (provided by this repo). Run python resize.py and get image folders 256/, 128/, 64/, 32/. Download the split.json.

Running the code

0. Preliminaries

• For train_liif.py or test.py, use --gpu [GPU] to specify the GPUs (e.g. --gpu 0 or --gpu 0,1).

• For train_liif.py, by default, the save folder is at save/_[CONFIG_NAME]. We can use --name to specify a name if needed.

1. celebAHQ experiments

Train: python train_liif.py --config configs/train-celebAHQ/[CONFIG_NAME].yaml.

Test: python test.py --config configs/test/test-celebAHQ-32-256.yaml --model [MODEL_PATH] (or test-celebAHQ-64-128.yaml for another task). We use epoch-best.pth in corresponding save folder.

Bibtex

@inproceedings{zhang2025sair,
  title={Sair: Learning semantic-aware implicit representation},
  author={Zhang, Canyu and Li, Xiaoguang and Guo, Qing and Wang, Song},
  booktitle={European Conference on Computer Vision},
  pages={319--335},
  year={2025},
  organization={Springer}
}