Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/lukasHoel/text2room

Text2Room generates textured 3D meshes from a given text prompt using 2D text-to-image models (ICCV2023).
https://github.com/lukasHoel/text2room

3d-generation diffusion-models mesh-generation text-to-image

Last synced: 11 days ago
JSON representation

Text2Room generates textured 3D meshes from a given text prompt using 2D text-to-image models (ICCV2023).

Awesome Lists containing this project

README 

        
# Text2Room

Text2Room generates textured 3D meshes from a given text prompt using 2D text-to-image models.



This is the official repository that contains source code for the ICCV 2023 paper [Text2Room](https://lukashoel.github.io/text-to-room/).



[[arXiv](https://arxiv.org/abs/2303.11989)] [[Project Page](https://lukashoel.github.io/text-to-room/)] [[Video](https://youtu.be/fjRnFL91EZc)]



![Teaser](docs/teaser.jpg "Text2Room")



If you find Text2Room useful for your work please cite:

```

@InProceedings{hoellein2023text2room,

    author    = {H\"ollein, Lukas and Cao, Ang and Owens, Andrew and Johnson, Justin and Nie{\ss}ner, Matthias},

    title     = {Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models},

    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},

    month     = {October},

    year      = {2023},

    pages     = {7909-7920}

}

```



## Prepare Environment



Create a conda environment:



```

conda create -n text2room python=3.9

conda activate text2room

pip install -r requirements.txt

```



Then install Pytorch3D by following the [official instructions](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md).

For example, to install Pytorch3D on Linux (tested with PyTorch 1.13.1, CUDA 11.7, Pytorch3D 0.7.2):



```

conda install -c fvcore -c iopath -c conda-forge fvcore iopath

pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"

```



Download the pretrained model weights for the fixed depth inpainting model, that we use:



- refer to the [official IronDepth implemention](https://github.com/baegwangbin/IronDepth) to download the files ```normal_scannet.pt``` and ```irondepth_scannet.pt```.

- place the files under ```text2room/checkpoints```



(Optional) Download the pretrained model weights for the text-to-image model:



- ```git clone https://huggingface.co/stabilityai/stable-diffusion-2-inpainting```

- ```git clone https://huggingface.co/stabilityai/stable-diffusion-2-1```

- ```ln -s  checkpoints```

- ```ln -s  checkpoints```



## Generate a Scene



As default, we generate a living room scene:



```python generate_scene.py```



Outputs are stored in ```text2room/output```.



### Generated outputs



We generate the following outputs per generated scene:



```

Mesh Files:

    /fused_mesh/after_generation.ply: generated mesh after the first stage of our method

    /fused_mesh/fused_final.ply: generated mesh after the second stage of our method

    /fused_mesh/x_poisson_meshlab_depth_y.ply: result of applying poisson surface reconstruction on mesh x with depth y

    /fused_mesh/x_poisson_meshlab_depth_y_quadric_z.ply: result of applying poisson surface reconstruction on mesh x with depth y and then decimating the mesh to have at least z faces

    

Renderings:

    /output_rendering/rendering_t.png: image from pose t, that was rendered from the final mesh

    /output_rendering/rendering_noise_t.png: image from a slightly different/noised pose t, that was rendered from the final mesh

    /output_depth/depth_t.png: depth from pose t, that was rendered from the final mesh

    /output_depth/depth_noise_t.png: depth from a slightly different/noised pose t, that was rendered from the final mesh



Metadata:

    /settings.json: all arguments used to generate the scene

    /seen_poses.json: list of all poses in Pytorch3D convention used to render output_rendering (no noise)

    /seen_poses_noise.json: list of all poses in Pytorch3D convention used to render output_rendering (with noise)

    /transforms.json: a file in the standard NeRF convention (e.g. see NeRFStudio) that can be used to optimize a NeRF for the generated scene. It refers to the rendered images in output_rendering (no noise).

```



We also generate the following intermediate outputs during generation of the scene:



```

    /fused_mesh/fused_until_frame_t.ply: generated mesh using the content until pose t

    /rendered/rendered_t.png: image from pose t, that was rendered from mesh_t

    /mask/mask_t.png: mask from pose t, that signals unobserved regions

    /mask/mask_eroded_dilated_t.png: mask from pose t, after applying erosion/dilation

    /rgb/rgb_t.png: image from pose t, that was inpainted with the text-to-image model

    /depth/rendered_depth_t.png: depth from pose t, that was rendered from mesh_t

    /depth/depth_t.png: depth from pose t, that was predicted/aligned from rgb_t and rendered_depth_t

    /rgbd/rgbd_t.png: combination of rgb_t and depth_t placed next to each other

```



### Create a scene from a fixed start-image



Already have an in-the-wild image, from which you want to start the generation?

Specify it as ```--input_image_path``` and the generated scene kicks-off from there.



```python generate_scene.py --input_image_path sample_data/0.png```



### Create a scene from another room type



Generate indoor-scenes of arbitrary rooms by specifying another ```--trajectory_file``` as input:



```python generate_scene.py --trajectory_file model/trajectories/examples/bedroom.json```



We provide a bunch of [example rooms](model/trajectories/examples).



### Customize Generation



We provide a highly configurable method. See [opt.py](model/utils/opt.py) for a complete list of the configuration options.



### Get creative!



You can specify your own prompts and camera trajectories by simply creating your own ```trajectory.json``` file.



#### Trajectory Format



Each ```trajectory.json``` file should satisfy the following format:



```

[

  {

    "prompt": (str, optional) the prompt to use for this trajectory,

    "negative_prompt": (str, optional) the negative prompt to use for this trajectory,

    "n_images": (int, optional) how many images to render between start and end pose of this trajectory,

    "surface_normal_threshold": (float, optional) the surface_normal_threshold to use for this trajectory

    "fn_name": (str, required) the name of a trajectory_function as specified in model/trajectories/trajectory_util.py

    "fn_args": (dict, optional) {

      "a": value for an argument with name 'a' of fn_name,

      "b": value for an argument with name 'b' of fn_name,

    },

    "adaptive": (list, optional) [

      {

        "arg": (str, required) name of an argument of fn_name that represents a float value,

        "delta": (float, required) delta value to add to the argument during adaptive pose search,

        "min": (float, optional) minimum value during search,

        "max": (float, optional) maximum value during search

      }

    ]

  },

  

  {... next trajectory with similar structure as above ...}

]

```



#### Adding new trajectory functions



We provide a bunch of predefined trajectory functions in [trajectory_util.py](model/trajectories/trajectory_util.py).

Each ```trajectory.json``` file is a combination of the provided trajectory functions. 

You can create custom trajectories by creating new combinations of existing functions.

You can also add custom trajectory functions in [trajectory_util.py](model/trajectories/trajectory_util.py).

For automatic integration with our codebase, custom trajectory functions should have the following pattern:



```

def custom_trajectory_fn(current_step, n_steps, **args):

    # n_steps: how many poses including start and end pose in this trajectory

    # current_step: pose in the current trajectory

    

    # your custom trajectory function here...



def custom_trajectory(**args):

    return _config_fn(custom_trajectory_fn, **args)

```



This lets you reference ```custom_trajectory``` as ```fn_name``` in a ```trajectory.json``` file.



## Render an existing scene



We provide a script that renders images from a mesh at different poses:



```python render_cameras.py -m  -c ```



where you can provide any cameras in the Pytorch3D convention via ```-c```.

For example, to re-render all poses used during generation and completion:



```

python render_cameras.py \

-m /fused_mesh/fused_final_poisson_meshlab_depth_12.ply \

-c /seen_poses.json

```



## Optimize a NeRF



We provide an easy way to train a NeRF from our generated scene.

We save a ```transforms.json``` file in the standard NeRF convention, that can be used to optimize a NeRF for the generated scene.

It refers to the rendered images in ```/output_rendering```.

It can be used with standard NeRF frameworks like [Instant-NGP](https://github.com/NVlabs/instant-ngp) or [NeRFStudio](https://github.com/nerfstudio-project/nerfstudio).



## Acknowledgements



Our work builds on top of amazing open-source networks and codebases. 

We thank the authors for providing them.



- [IronDepth](https://github.com/baegwangbin/IronDepth) [1]: a method for monocular depth prediction, that can be used for depth inpainting.

- [StableDiffusion](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) [2]: a state-of-the-art text-to-image inpainting model with publicly released network weights.



[1] IronDepth: Iterative Refinement of Single-View Depth using Surface Normal and its Uncertainty, BMVC 2022, Gwangbin Bae, Ignas Budvytis, and Roberto Cipolla



[2] High-Resolution Image Synthesis with Latent Diffusion Models, CVPR 2022, Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer