My3DGen: A Scalable Personalized 3D Generative Model
🌟 WACV 2025 Oral Presentation 🌟

Luchao Qi¹ Jiaye Wu² Annie N. Wang¹ Shengze Wang¹ Roni Sengupta²

¹University of North Carolina at Chapel Hill
²University of Maryland

[Project Page] | [Video] | [Arxiv]

Introduction

This is the official implementation of My3DGen: A Scalable Personalized 3D Generative Model. We personalize a pretrained GAN-based model (EG3D) using a few (~50) selfies of an individual without full finetuning, enabling scalable personalization in a real-world scenario.

The framework of the proposed My3DGen model:

Installation

Environment

We build our code on top of the EG3D repository. Please refer to EG3D to setup the environment & checkpoints and run the following commands after setup the EG3D environment.

conda activate eg3d
pip install lpips

Dataset Preparation

Images

We use the dataset of celebrities from images-of-celebs.

We follow EG3D's processing pipeline to preprocess the images-of-celebs dataset.

Latent codes

We originally follow the oneThousand1000/EG3D-projector: An unofficial inversion code of eg3d. to generate the latent codes of the images-of-celebs dataset. You may use more recent SOTA inversion methods to generate the latent codes e.g.

[2303.13497] TriPlaneNet: An Encoder for EG3D Inversion
[2303.12326] Make Encoder Great Again in 3D GAN Inversion through Geometry and Occlusion-Aware Encoding

└── datasets
    ├── Barack
    │   ├── image_0.png
    │   ├── image_0_latent.npy
    │   ├── ...
    │   ├── image_n.png
    │   ├── image_n_latent.npy
    │   └── dataset.json
    ├── Morgan
    │   ├── image_0.png
    │   ├── image_0_latent.npy
    │   ├── ...
    │   ├── image_n.png
    │   ├── image_n_latent.npy
    │   └── dataset.json
    ├── ...
    └── Celebrity_N
        ├── image_0.png
        ├── image_0_latent.npy
        ├── ...
        ├── image_n.png
        ├── image_n_latent.npy
        └── dataset.json

Training

Go to the ./eg3d directory and modify the following command to train the model:

python train.py --outdir=<output_dir> \
--data=<dataset_dir e.g. ./datasets/Barack> \
--resume=<pretrained_model_path e.g. ./networks/ffhqrebalanced512-128.pkl> \
--cfg=<cfg_file e.g. ffhq> \
--batch=<batch_size e.g. 4> \
--gpus=<num_gpus e.g. 4> \
--snap=<num_snapshots e.g. 5> \
--kimg=<num_kimg e.g. 500> \
--lora=<lora_rank e.g. 1> \
--lora_alpha=<lora_alpha e.g. 1> \
--adalora=False \
--freeze_render=False

Testing

After training, you can run various downstream tasks.

For inversion, you can refer to danielroich/PTI: Official Implementation for "Pivotal Tuning for Latent-based editing of Real Images" (ACM TOG 2022) https://arxiv.org/abs/2106.05744.

For editing, you can refer to google/mystyle.

Citation

If you find this work useful for your research, please kindly cite our paper:

@InProceedings{Qi_2025_WACV,
    author    = {Qi, Luchao and Wu, Jiaye and Wang, Annie N. and Wang, Shengze and Sengupta, Roni},
    title     = {My3DGen: A Scalable Personalized 3D Generative Model},
    booktitle = {Proceedings of the Winter Conference on Applications of Computer Vision (WACV)},
    month     = {February},
    year      = {2025},
    pages     = {961-972}
}