Dynamic-Aware Gaussian Generation

• Dynamic-aware Gaussian generation is a framework that models both static and dynamic scene elements using anisotropic 3D Gaussian primitives, enabling accurate reconstruction and simulation.
• It employs differentiable splatting and volumetric compositing to ensure temporally consistent rendering with effective occlusion handling and real-time performance.
• The approach integrates multimodal loss optimization and LLM-guided trajectory editing, facilitating interactive scene manipulation in applications like autonomous driving and AR/VR.

Dynamic-aware Gaussian generation is a unified designation for scene modeling and synthesis frameworks that utilize explicit or parameterized Gaussian primitives to represent both static and dynamic entities, with models that are structurally and algorithmically equipped to differentiate, reconstruct, and render temporally-evolving components. Recent literature demonstrates that this class of methods is pivotal for high-fidelity, temporally-consistent reconstruction and real-time editable simulation of dynamic environments, as in autonomous driving, immersive graphics, and dynamic SLAM.

1. Core Parametrizations: Static and Dynamic Gaussians

Dynamic-aware Gaussian frameworks typically distinguish between static scene content and dynamic entities at the level of Gaussian parameterization. The foundational unit is the anisotropic 3D Gaussian for static points, parameterized by mean $\mu_s \in \mathbb{R}^3$, covariance $\Sigma_s \in \mathbb{R}^{3\times3}$, view-dependent color coefficients (e.g., spherical harmonics) $c_s$, and opacity $\alpha_s \in [0,1]$. The unnormalized spatial density is: $$p(\mathbf{l} \mid \mu_s, \Sigma_s) = \exp\big(-\tfrac{1}{2}(\mathbf{l} - \mu_s)^\mathsf{T} \Sigma_s^{-1} (\mathbf{l} - \mu_s)\big)$$ This Gaussian is projected into 2D screen-space and contributes to pixel color according to its depth, opacity, and color basis.

Dynamic content is decomposed as a set of objects, each represented as a node in a composite dynamic Gaussian graph $H = \langle O, G_d, M, P, A, T\rangle$, where $G_d(o)$ is the set of Gaussians belonging to object $o$ and $M_o \in SE(3)$ tracks the pose. Dynamic object positions $P_o(t)$ and orientations $A_o(t)$ are explicit functions of time, enabling accurate registration, occlusion reasoning, and motion-based compositing.

Compositing for rendering is performed as a union of static and dynamic Gaussians: $$G_\mathrm{comp} = G_s \cup \bigcup_{o \in O} G_d(o)$$ with explicit opacity adjustment by object-to-camera distance.

This separation undergirds the tractability and controllability of rendering, editing, and motion simulation in dynamic-aware frameworks (Xiong et al., 28 Aug 2025).

2. Differentiable Rendering and Volume Compositing

Rendering of dynamic-aware Gaussian scenes is handled via differentiable splatting and volumetric compositing. Each 3D Gaussian is reprojected as a 2D Gaussian with pixel-level covariance: $$\widetilde{\Sigma} = J E \Sigma E^\mathsf{T} J^\mathsf{T}$$ where $E$ is the world–camera extrinsic, and $J$ is the Jacobian of pinhole projection.

Per-pixel compositional weighting is dictated by depth-sorted accumulation: $$T_i = \prod_{j<i} (1-\alpha_j), \quad w_i = \alpha_i\,T_i, \quad \widehat{C}(\mathbf{x}) = \sum_{i=1}^K w_i C_i$$ These equations allow for efficient GPU implementation with fully differentiable supervision during optimization.

Occlusion and correct front-to-back compositing are ensured by sorting all Gaussians—regardless of static or dynamic status—along the camera ray prior to accumulation (Xiong et al., 28 Aug 2025).

3. Joint Optimization: Multimodality, Robustness, and Depth Priors

The optimization of dynamic-aware Gaussian fields requires loss function design that simultaneously enforces photometric consistency (across time and view), structural robustness, and multi-modal alignment (e.g., depth or LiDAR priors):

• Tile-SSIM Loss assesses local photometric similarity in tiles:

  $$\mathcal{L}_{\mathrm{TSSIM}}(\delta) = 1 - \frac{1}{Z} \sum_{z=1}^Z \mathrm{SSIM}(\Psi(\widehat{I}_z), \Psi(I_z))$$

• Robust Photometric Loss uses robust penalties on image residuals:

  $$\mathcal{L}_\mathrm{Robust}(\delta) = \kappa(\|\widehat{I} - I\|_2)$$

• LiDAR Depth-Prior Loss regularizes reconstructed Gaussian centroids to match multi-frame point clouds:

  $$\mathcal{L}_\mathrm{LiDAR}(\delta) = \frac{1}{S}\sum_{s=1}^S \|P(G_\mathrm{comp})_s - L_s\|^2$$

The total loss combines these terms with schedule-controlled weights, and adopts variable learning rates for static vs. dynamic Gaussians, reflecting their respective temporal and spatial fidelity requirements (Xiong et al., 28 Aug 2025).

4. Scene Editing and Controllable Dynamics via Trajectory Embedding

Dynamic-aware Gaussian methods excel in allowing post-optimization, training-free editing of dynamic content. Control over dynamic object behavior is realized through LLM-assisted or rule-based trajectory sampling:

• After reconstructing $G_\mathrm{comp}$ for $t=0$, new dynamic Gaussians $G_\mathrm{new}$ are initialized and given a start pose.
• An LLM generates a text-to-trajectory function:

  $$\mathrm{Traj}_\mathrm{pred} = \mathrm{LLM}(P_0, \mathrm{dir}_\mathrm{sky}, \mathrm{desc})$$

with per-frame center updates $$P_\mathrm{new}(t+1) = P_\mathrm{new}(t) + \mathrm{Traj}_\mathrm{pred}(t)$$.

• During playback, $G_\mathrm{new}(t)$ are transformed under $M_\mathrm{new}(t)$ and composited with $G_\mathrm{comp}(t)$.

Particle-based effects (e.g., rain, snow) are similarly handled with parametric trajectory functions, optionally lifting 2D edits into 3D space via geometry-aware reprojection (Xiong et al., 28 Aug 2025).

Editing, addition, or re-texturing of dynamic entities occurs in zero-shot mode, without retraining, leveraging the explicit graph structure and instanced Gaussian sets.

5. Structural Properties: Composite Dynamic Graph and Scene Coherence

Dynamic-aware Gaussian generation leverages a composite scene graph $H$ that tracks object-wise Gaussian sets, motion trajectories, and temporal transformation matrices. This explicit graph structure confers crucial advantages:

• Occlusion Handling: Objects are layered and sorted dynamically to maintain correct depth ordering.
• Motion Coherence: Dynamic objects' Gaussians follow time-sampled, SE(3)-parametrized local coordinate frames, ensuring consistency of geometric and appearance attributes across time.
• Incremental Background Modeling: Static background evolves by incremental addition of Gaussians (e.g., from densified LiDAR sweeps or image-based densification), producing a large-scale, stable background field.

This compositional separation underpins the ability to reconstruct, edit, and simulate driving scenes with multi-object temporal coherence and high visual fidelity (Xiong et al., 28 Aug 2025).

6. Implementation Considerations and Performance

The methodology is engineered for scalable, fast optimization and rendering:

• Composing $>10^4$–$10^6$ Gaussians with efficient GPU rasterization.
• Learning-rate Scheduling: Static and dynamic Gaussian graphs are optimized at different rates for stability.
• Real-time or Near Real-time Rendering: Differentiable rendering pipeline supports rapid scene synthesis and interactive editing.
• Resource Requirements: Multi-modal loss fusion (RGB, LiDAR) and optimization across time steps demands significant but tractable GPU memory and computational throughput (multi-GPU recommended for city-scale scenes).
• Scalability: The approach supports training-free editing and dynamic simulation via LLM-guided trajectories or procedural particle motion.

Empirical results demonstrate state-of-the-art performance in dynamic scene reconstruction, photorealistic surround-view synthesis, and editable simulation across large-scale and diverse scenarios (Xiong et al., 28 Aug 2025).

7. Connections to Related Paradigms and Applications

Dynamic-aware Gaussian generation integrates concepts from explicit 3D scene representation, instance-level dynamic graph modeling, multi-view optimization with geometric and photometric objectives, and interactive scene semantics. It generalizes and extends prior techniques such as Gaussian Splatting for static scenes, NeRF-based dynamic modeling, and hybrid geometrically-aware dynamic splatting. The integration of LLMs for trajectory generation opens avenues for semantic-level, instruction-driven scene manipulation and simulation.

Principal applications include photorealistic and physically consistent surround-view generation for autonomous vehicles, synthetic data augmentation, scene editing for simulation or AR/VR, and temporally-coherent dataset synthesis for robust perception under dynamic conditions.

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In summary, dynamic-aware Gaussian generation encompasses a suite of structural, algorithmic, and optimization innovations for jointly modeling, rendering, and editing both static and dynamic elements in temporally-evolving scenes by leveraging composite Gaussian fields, explicit dynamic graphs, multi-modal loss supervision, and controllable editing interfaces. The resulting pipelines deliver high scene fidelity, explicit motion structure, and real-time dynamic editing capabilities (Xiong et al., 28 Aug 2025).