From Video to Volumetric: The Rise of 4D Gaussian Splatting

By early 2026, the boundary between video and 3D space has effectively dissolved. While 2024 was the year of static Gaussian Splatting, 2025 introduced 4D Gaussian Splatting (4DGS)—the ability to capture and reconstruct dynamic, time-evolving volumetric scenes with unprecedented fidelity.

What is 4D Gaussian Splatting?

Unlike traditional meshes or even static splats, 4DGS represents a scene as a collection of anisotropic Gaussians that change their position, shape, and color over a temporal dimension ($t$). This allows for:

1. Photorealistic Dynamic Reconstructions: Capturing a person dancing or a car moving and being able to fly a virtual camera around them while the action is happening.
2. Real-Time Playback: Modern 4DGS engines can now render complex volumetric sequences at 140+ FPS on consumer-grade hardware (RTX 50-series).
3. Temporal Continuity: Unlike AI video models (like Sora 2) which generate new frames, 4DGS reconstructs the actual spatial reality of a recorded event.

Temporal Encoding Architectures

The current state-of-the-art has shifted from early HexPlane (2024) architectures to more efficient 4D Sparse Voxel Grids. The primary approach involves Deformable GS, where a tiny MLP predicts the deformation $(\Delta \mu, \Delta s, \Delta q)$ of Gaussians from a canonical frame based on a time embedding.

By using Motion Coefficients, we can represent complex trajectories as low-rank approximations, significantly reducing the memory footprint. This enables long-duration volumetric sequences to fit within standard 24GB VRAM limits.

Physics-Grounded Dynamics (Phys-Splat)

A major breakthrough in late 2025 was the integration of physical constraints into the optimization loop, commonly referred to as Phys-Splat. By incorporating elements of the Material Point Method (MPM), we ensure that movement looks natural.

Key physical parameters like Young's Modulus (stiffness) and Poisson's Ratio (compressibility) are now optimized alongside Spherical Harmonic (SH) coefficients. This prevents "ghosting" or "popping" artifacts by maintaining volume consistency and realistic material behavior during fast, non-rigid motion.

The 2026 Tech Stack: Unreal Engine 6 & RTX 5090

The current ecosystem revolves around high-performance implementations that integrate seamlessly with modern hardware.

Training & Rendering Benchmarks (February 2026)

AstraML's internal pipeline utilizes custom CUDA kernels optimized for tiled temporal culling, allowing us to skip rendering splats that are occluded or temporally inactive.

Key Advantages over NeRFs

• Training Speed: What used to take hours of optimization now takes under 10 minutes for a 30-second sequence.
• Editability: Because splats are discrete "blobs" of data, they are significantly easier to manipulate, relight, and compose within 3D engines like Unreal Engine 6.

Production Impact

For creative directors at AstraML, 4D Splatting represents the ultimate "Volumetric Video" pipeline. We are no longer limited to the camera angles we shot on set. We can record a performance once and decide the cinematography in post-production, within a fully 3D environment.

Frequently Asked Questions

How fast is 4D Gaussian Splatting? Modern implementations like AstraML's custom kernel can render 4D volumetric scenes at over 145 FPS on an NVIDIA RTX 5090, making it fully viable for real-time applications.

What is the difference between NeRF and 4DGS? While NeRFs (Neural Radiance Fields) offer high quality, they are often slow to train and render. 4DGS uses discrete splats, allowing for faster training (minutes vs. hours) and easier editing in game engines like Unreal Engine 6.

Does 4DGS support physics simulations? Yes. With techniques like Phys-Splat, 4D Gaussian Splatting can incorporate physical properties (stiffness, compressibility), allowing digital avatars to react realistically to forces and collisions.

Stay tuned for our upcoming workshop on integrating 4DGS into Unreal Engine 6.