base on [CVPR 2024 Highlight] PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics # PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics ### [[Project Page](https://xpandora.github.io/PhysGaussian/)] [[arXiv](https://arxiv.org/abs/2311.12198)] [[Video](https://www.youtube.com/watch?v=V96GfcMUH2Q)] Tianyi Xie¹\*, Zeshun Zong¹\*, Yuxing Qiu¹\*, Xuan Li¹\*, Yutao Feng^2,3, Yin Yang³, Chenfanfu Jiang¹<br> ¹University of California, Los Angeles, ²Zhejiang University, ³University of Utah <br> *Equal contributions  Abstract: *We introduce PhysGaussian, a new method that seamlessly integrates physically grounded Newtonian dynamics within 3D Gaussians to achieve high-quality novel motion synthesis. Employing a customized Material Point Method (MPM), our approach enriches 3D Gaussian kernels with physically meaningful kinematic deformation and mechanical stress attributes, all evolved in line with continuum mechanics principles. A defining characteristic of our method is the seamless integration between physical simulation and visual rendering: both components utilize the same 3D Gaussian kernels as their discrete representations. This negates the necessity for triangle/tetrahedron meshing, marching cubes, ''cage meshes,'' or any other geometry embedding, highlighting the principle of ''what you see is what you simulate (WS2).'' Our method demonstrates exceptional versatility across a wide variety of materials--including elastic entities, plastic metals, non-Newtonian fluids, and granular materials--showcasing its strong capabilities in creating diverse visual content with novel viewpoints and movements.* ## News - [2024-03-27] Release a Colab notebook for quick start.[](https://colab.research.google.com/drive/165WAoLw2HK4WifsA4Ngqgeke6gJVQXDm?usp=sharing) - [2024-03-03] Code Release. - [2024-02-27] Our paper has been accpetd by CVPR 2024! - [2023-12-20] Our [MPM solver code](https://github.com/zeshunzong/warp-mpm) is open sourced! ## Cloning the Repository This repository uses original gaussian-splatting as a submodule. Use the following command to clone: ```shell git clone --recurse-submodules [email protected]:XPandora/PhysGaussian.git ``` ## Setup ### Python Environment To prepare the Python environment needed to run PhysGaussian, execute the following commands: ```shell conda create -n PhysGaussian python=3.9 conda activate PhysGaussian pip install -r requirements.txt pip install -e gaussian-splatting/submodules/diff-gaussian-rasterization/ pip install -e gaussian-splatting/submodules/simple-knn/ ``` By default, We use pytorch=2.0.0+cu118. ### Quick Start We provide several pretrained [Gaussian Splatting models](https://drive.google.com/drive/folders/1EMUOJbyJ2QdeUz8GpPrLEyN4LBvCO3Nx?usp=drive_link) and their corresponding `.json` config files in the `config` directory. To simulate a reconstructed 3D Gaussian Splatting scene, run the following command: ```shell python gs_simulation.py --model_path <path to gs model> --output_path <path to output folder> --config <path to json config file> --render_img --compile_video ``` The images and video results will be saved to the specified output_path. If you want a quick try, run: ```shell pip install gdown bash download_sample_model.sh python gs_simulation.py --model_path ./model/ficus_whitebg-trained/ --output_path output --config ./config/ficus_config.json --render_img --compile_video --white_bg ``` Hopefully, you will see a video result like this: <img src="./demo/ficus.gif" width="300"/> ## Custom Dynamics To generate custom dynamics, follow these guidelines: ### Gaussian Splatting Reconstruction Begin by reconstructing a 3D GS scene as per [Gaussian Splatting](https://github.com/graphdeco-inria/gaussian-splatting). ### Data Preprocessing Before simulating Gaussian kernels as continuum particles, perform the following preprocessing steps: 1. Remove Gaussian kernels with low opacity. 2. Rotate the 3D scene to make it align with the coordinate plane (e.g., bottom surface parallel to the xy plane). 3. Define a cuboid simulation area. 4. Center and scale the simulation area within a unit cube. 5. Optionally, fill internal voids with particles. Related parameters, such as rotation axis and degree, should be provided in the config file. For [Nerf Synthetic Dataset](https://drive.google.com/file/d/18JxhpWD-4ZmuFKLzKlAw-w5PpzZxXOcG/view?usp=drive_link), the reconstructed results typically already align with the axis. For custom datasets, we use 3D software, e.g. [Houdini](https://www.sidefx.com/), to view the distribution of the Gaussian kernels and determine how to rotate and select the scene for simulation readiness. ### Config Json File A single `.json` file should detail all data preprocessing and simulation parameters for each scene. Key parameters include: - Data Preprocessing Parameters: - `opacity_threshold`: Filters out Gaussian kernels with opacity below this threshold. - `rotation_degree (list)` and `rotation_axis (list)`: Rotate the scene to align with the grid. - `sim_area (list)`: Choose the particles within a bounding box for simulation. The expected format is `[xmin, xmax, ymin, ymax, zmin, zmax]`. - `particle_filling (dict)`: Specify a cubic area to fill internal particles. Tuning ```density_threshold``` and ```search_threshold``` is usually needed for optimal filling results. See more details below. - Simulation Parameters: - `material`: Available material types include `jelly`, `metal`, `sand`, `foam`, `snow` and `plasticine`. - `E`: Young's modulus - `nu`: Poisson's Ratio - `density`: Material density - `g`: Gravity - `substep_dt`: Simulation time step size - `n_grid`: MPM grid size - `boundary_conditions (list)`: Boundary conditions can be enforced on either particles or grids, allowing manipulation of Gaussian kernels via external forces. - Export Parameters: - `frame_dt`: Duration of each frame - `frame_num`: Total number of frames to export - `default_camera_index`: Camera view index from the training set Please see sample config files under the `config` folder for reference. #### Particle Filling Optionally, we employ a ray-collision-based method to detect inner grids for particle filling. To use this, specify the following parameters: - `n_grid`: Particle filling grid size. - `density_threshold`: Grid cells with density above this threshold will be treated as part of the surface shell. - `search_exclude_direction`: A parameter (list of ints) for internal filling condition 1 in PhysGaussian. We won't cast rays in these excluded directions. The mapping between ints and directions is: 0, 1, 2, 3, 4, 5 (+x, -x, +y, -y, +z, -z). - `ray_cast_direction`: tA parameter for internal filling condition 2 in PhysGaussian. Along this direction, we will detect the number of collision times. The mapping between ints and directions is the same as `search_exclude_direction`. - `max_particles_per cell`: The number of particles to fill for each grid cell. - `boundary`: Specify a well-reconstructed cubic area to perform particle filling. Note: This particle filling algorithm is sensitive to Gaussian kernel distribution and may produce unsatisfying filling results if Gaussians are too noisy. #### Boundary Condition To fix or move the reconstructed object, specify the boundary condition either on grids or particles. Some commonly used boundary condition types include: - `bounding_box`: Prevents particles from moving outside the MPM simulation area. - `cuboid`: Enforces a boundary condition on the grid. Also specify other necessary parameters: - `point`: Center of the cubic area, e.g. `[1, 1, 1]` - `size`: Size of the cubic area (half of the width, height and depth), e.g. `[0.2, 0.2, 0.2]` - `vecloticy`: Velocity assigned to the grids, e.g. `[0, 0, 0]` - `start_time` and `end_time`: Time duration of this boundary condition - `enforce_particle_translation`: Enforces a boundary condition on particles with parameters similar to those for grids. ## Citation ``` @article{xie2023physgaussian, title={PhysGaussian: Physics-Integrated 3D Gaussians for Generative Dynamics}, author={Xie, Tianyi and Zong, Zeshun and Qiu, Yuxing and Li, Xuan and Feng, Yutao and Yang, Yin and Jiang, Chenfanfu}, journal={arXiv preprint arXiv:2311.12198}, year={2023}, } ``` ", Assign "at most 3 tags" to the expected json: {"id":"5246","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"