EscherNet: A Generative Model for Scalable View Synthesis

Xin Kong · Shikun Liu · Xiaoyang Lyu · Marwan Taher · Xiaojuan Qi · Andrew J. Davison

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EscherNet is a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with the camera positional encoding (CaPE), allowing precise and continuous relative control of the camera transformation between an arbitrary number of reference and target views.

## Install ``` conda env create -f environment.yml -n eschernet conda activate eschernet ``` ## Demo Run demo to generate randomly sampled 25 novel views from (1,2,3,5,10) reference views: ```commandline bash eval_eschernet.sh ``` ## Camera Positional Encoding (CaPE) CaPE is applied in self/cross-attention for encoding camera pose info into transformers. The main modification is in `diffusers/models/attention_processor.py`. To quickly check the implementation of CaPE (6DoF and 4DoF), run: ``` python CaPE.py ``` ## Training ### Objaverse 1.0 Dataset Download Zero123's Objaverse Rendering data: ```commandline wget https://tri-ml-public.s3.amazonaws.com/datasets/views_release.tar.gz ``` Filter Zero-1-to-3 rendered views (empty images): ```commandline cd scripts python objaverse_filter.py --path /data/objaverse/views_release ``` ### Launch training Configure accelerator (8 A100 GPUs, bf16): ```commandline accelerate config ``` Choose 4DoF or 6DoF CaPE (Camera Positional Encoding): ```commandline cd 4DoF or 6DoF ``` Launch training: ```commandline accelerate launch train_eschernet.py --train_data_dir /data/objectverse/views_release --pretrained_model_name_or_path runwayml/stable-diffusion-v1-5 --train_batch_size 256 --dataloader_num_workers 16 --mixed_precision bf16 --gradient_checkpointing --T_in 3 --T_out 3 --T_in_val 10 --output_dir logs_N3M3B256_SD1.5 --push_to_hub --hub_model_id ***** --hub_token hf_******************* --tracker_project_name eschernet ``` For monitoring training progress, we recommand [wandb](https://wandb.ai/site) for its simplicity and powerful features. ```commandline wandb login ``` Offline mode: ```commandline WANDB_MODE=offline python xxx.py ``` ## Evaluation We provide [raw results](https://huggingface.co/datasets/kxic/EscherNet-Results) and two checkpoints [4DoF](https://huggingface.co/kxic/eschernet-4dof) and [6DoF](https://huggingface.co/kxic/eschernet-6dof) for easier comparison. ### Datasets ##### [GSO Google Scanned Objects](https://app.gazebosim.org/GoogleResearch/fuel/collections/Scanned%20Objects%20by%20Google%20Research) [GSO30](https://huggingface.co/datasets/kxic/EscherNet-Dataset/tree/main): We select 30 objects from GSO dataset and render 25 randomly sampled novel views for each object for both NVS and 3D reconstruction evaluation. ##### [RTMV](https://huggingface.co/datasets/kxic/EscherNet-Dataset/tree/main) We use the 10 scenes from [google_scanned.tar](https://drive.google.com/drive/folders/1cUXxUp6g25WwzHnm_491zNJJ4T7R_fum) under folder `40_scenes` for NVS evaluation. ##### [NeRF_Synthetic](https://huggingface.co/datasets/kxic/EscherNet-Dataset/tree/main) We use the all 8 NeRF objects for 2D NVS evaluation. ##### [Franka16](https://huggingface.co/datasets/kxic/EscherNet-Dataset/tree/main) We collected 16 real world object-centric recordings using a Franka Emika Panda robot arm with RealSense D435i Camera for real world NVS evaluation. ##### [Text2Img](https://huggingface.co/datasets/kxic/EscherNet-Dataset/tree/main) We collected Text2Img generation results from internet, [Stable Diffusion XL](https://github.com/Stability-AI/generative-models) (1 view) and [MVDream](https://github.com/bytedance/MVDream) (4 views: front, right, back, left) for NVS evaluation. ### Novel View Synthesis (NVS) To get 2D Novel View Synthesis (NVS) results, set `cape_type, checkpoint, data_type, data_dir` and run: ```commandline bash ./eval_eschernet.sh ``` Evaluate 2D metrics (PSNR, SSIM, LPIPS): ```commandline cd metrics python eval_2D_NVS.py ``` ### 3D Reconstruction We firstly generate 36 novel views with `data_type=GSO3D` by: ```commandline bash ./eval_eschernet.sh ``` Then we adopt [NeuS](https://github.com/Totoro97/NeuS) for 3D reconstruction: ```commandline export CUDA_HOME=/usr/local/cuda-11.8 pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch cd 3drecon python run_NeuS.py ``` Evaluate 3D metrics (Chamfer Distance, IoU): ```commandline cd metrics python eval_3D_GSO.py ``` ## Gradio Demo TODO. To build locally: ```commandline python gradio_eschernet.py ``` ## Acknowledgement We have intensively borrow codes from the following repositories. Many thanks to the authors for sharing their codes. - [Zero-1-to-3](https://github.com/cvlab-columbia/zero123) - [SyncDreamer](https://github.com/liuyuan-pal/SyncDreamer) - [MVDream](https://github.com/bytedance/MVDream) - [NeuS](https://github.com/Totoro97/NeuS) ## Citation If you find this work useful, a citation will be appreciated via: ``` @article{kong2024eschernet, title={EscherNet: A Generative Model for Scalable View Synthesis}, author={Kong, Xin and Liu, Shikun and Lyu, Xiaoyang and Taher, Marwan and Qi, Xiaojuan and Davison, Andrew J}, journal={arXiv preprint arXiv:2402.03908}, year={2024} } ```