4D-GS combines 3D Gaussians and 4D neural voxels, using a decomposed neural voxel encoding algorithm and a lightweight MLP to predict Gaussian deformations at novel timestamps, enabling real-time rendering of dynamic scenes.
Efficiently builds Gaussian features from 4D neural voxels, enabling fast rendering of dynamic scenes.
Predicts Gaussian deformations at novel timestamps, allowing for real-time rendering of complex motions.
Enables fast and accurate rendering of dynamic scenes at high image resolutions.
Maintains high training and storage efficiency, making it suitable for applications requiring fast and accurate rendering.
Achieves comparable or better quality than previous state-of-the-art methods, with real-time rendering at high image resolutions.
Real-time rendering of dynamic scenes for applications such as video games, simulations, and virtual reality.
Fast and accurate rendering of complex motions for applications such as physics-based simulations and animation.
High-quality rendering of dynamic scenes for applications such as film and video production.
Train the 4D-GS model using a dataset of dynamic scenes.
Use the trained model to render dynamic scenes in real-time.
Adjust the model parameters to achieve the desired level of rendering quality and efficiency.