From: hu-po
Gaussian Splatting, particularly its optimization for 3D reconstruction, is a burgeoning field in generative 3D modeling. This technique involves representing a 3D scene or object as a collection of 3D Gaussian “splats,” each with its own position, rotation, color, and opacity [03:06:59]. Unlike implicit representations like Neural Radiance Fields (NeRFs), Gaussian Splats offer a more explicit and potentially higher-capacity representation for complex scenes [03:10:05].
Core Concepts
4D Gaussian Splatting
Traditional 3D Gaussian Splatting creates static 3D scenes. 4D Gaussian Splatting extends this by adding a time dimension, allowing for the representation of dynamic content, such as a moving object or a sequence of 3D scenes [00:36:18]. This means the properties of each Gaussian (like position and rotation) can change over time, enabling animation [03:32:11].
Gaussian Flow
A novel concept introduced to optimize 4D Gaussian Splatting is “Gaussian flow” [00:36:50]. This connects the dynamics of 3D Gaussians with 2D pixel velocities between consecutive video frames [00:36:53]. It’s efficiently obtained by splatting Gaussian dynamics into the image space, making the process differentiable [00:37:00]. This enables dynamic supervision from optical flow, which is a pixel-level representation of movement in a video [00:37:05] [00:39:15].
The optimization process for Gaussian flow typically involves:
- Initialization: Since 3D Gaussians need initial XYZ positions, a pre-trained model like “0123” (using Score Distillation Sampling, SDS) can be used to set the initial starting positions of these Gaussian points [00:37:50].
- Frame-by-frame optimization: Gradients are pushed directly into the Gaussians using SDS based on photometric consistency [00:38:44].
- Optical Flow Integration: Optical flow, while sometimes a “weakest link” due to its pixel-level nature and difficulty with uniform colors [02:23:17], provides a crucial signal. The Gaussian flow is distilled by combining the optical flow with the movement of Gaussians through a weighted sum, ensuring consistency between the rendered Gaussian splat and the video’s optical flow [00:43:11] [00:45:59]. This allows direct dynamic supervision of 3D Gaussian dynamics [00:45:31].
Advantages and Comparisons
Representational Capacity
One of the key advantages of Gaussian Splats over NeRFs is their higher representational capacity [03:10:05]. While a NeRF implicitly stores scene information within a tiny Multi-Layer Perceptron (MLP), Gaussian Splats explicitly define individual Gaussian primitives with distinct attributes (position, rotation, color, opacity). This explicit nature allows for potentially finer details and better quality reconstructions [03:10:05].
Efficiency and Real-time Rendering
Gaussian Splatting is noted for its efficiency, particularly for real-time rendering and handling dynamic content [03:17:14]. Models like the “Large Gaussian Reconstruction Model” (GRM) are designed as feedforward Transformer-based models, translating input pixels into pixel-aligned Gaussians for rapid reconstruction from sparse view images (around 0.1 seconds, though hardware dependent) [02:52:02] [03:12:32].
Overcoming Limitations
Gaussian Splatting also helps address issues like “color drifting” in 4D content [00:37:25]. The use of multi-view information, often derived from video diffusion models, helps overcome the “Yanis problem” (inconsistent generation of different views, especially the back of an object), leading to more consistent 3D reconstructions [02:54:25].
Implications for Future Technologies
The future of generative 3D models is likely to heavily involve Gaussian Splatting. The ability to generate billions of splats from vast video datasets (like YouTube) could lead to diffusion models that generate Gaussian Splatting directly from noise [00:57:33] [00:57:51]. This could enable advanced applications like text-conditioned or speech-conditioned 4D Gaussian Splat diffusion models, potentially powering future VR headset experiences by 2030 [00:57:57].
It is predicted that advancements in video diffusion models, such as Sora, will feed into these Gaussian Splatting techniques, leading to even higher quality and more consistent 3D generation [03:15:01].