Physics-embedded NN structure for machine learning application in Computer Graphics cloth animation. Direct PBS features are encoded into the model, with functional extensions to integrate extra visual improvements.
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Physics-based cloth simulation: mass-spring system
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Comprehensive force interaction
Internal: elastic, damping, and bending
External: gravity, pressure, friction, and air drag
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Collision handling and boundary constraints
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Deep Learning application for specific PDE system
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CNN representation of spatial correlations
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Conditional programming with GPU-parallelized boolean tensor
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ML acceleration for real-time animation and rendering
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Integrable framework for prevailing AI techniques on folds and wrinkle enhancement
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python
$\approx$ 3.10 -
taichi
$\approx$ 1.4 -
pytorch
$\approx$ 2.1.1
Cuda or CPU backends for simulation and learning; Vulkan available for rendering
python cloth_press.py
python cloth_hang.py
python cloth_ball.py
python groundTruth_press.py [DATA_SAVE_PATH]
python nn_cloth_train.py [training_data_set.npz] [MODEL_SAVE_PATH] [starting_model.pt](Optional)
python nn_cloth_infer.py [initial_state.npz] [evaluated_model.pt] [infered_result_name.npz]
python nn_cloth_check.py [checked_data.npz]
python plotloss.py [loss_log]
python viewmodel.py [model.pt]
python cloth_view.py [NN_result.npz]
python compare.py [ground_truth.npz] [NN_result.npz]
-some trained models are provided
-logs are provided to check loss track and time consumption
- Integrate with additional forces by PBS (e.g., turbulent flow).
- Add self-collision detection and response: Bounding Volume Hierarchy & vertex-triangle, edge-edge detection.
- Incorporate sub-NN to refine cloth wrinkles under low-reso mesh.