Dynamic Radiance Fields

• Dynamic radiance fields are neural scene representations that capture both static geometry and complex time-varying phenomena like motion and illumination changes.
• They employ varied methodologies—including implicit neural networks, explicit voxel grids, and point-based approaches—to handle non-rigid deformations and complex motion modeling.
• Advanced rendering techniques and hardware optimizations enable interactive and photorealistic scene synthesis for applications in AR/VR, telepresence, and dynamic video.

Dynamic radiance fields are neural scene representations that generalize the neural radiance field (NeRF) paradigm to model not only static geometry and appearance but also complex time-varying phenomena such as object motion, deformation, illumination changes, and stylization. These approaches enable synthesis of novel spatial and temporal views from high-dimensional 4D (space-time) or even higher-dimensional data acquired with monocular or multi-view video. The field is characterized by a diverse set of architectures and training methodologies, incorporating motion modeling, explicit geometric priors, physics-based constraints, hardware-specific optimizations, and stylization in both appearance and geometry.

1. Formulation and Representation

A dynamic radiance field models the color and density at any given spatial location, time, and (optionally) view direction. This function is commonly defined as:

$$\mathcal{F}_\text{DYN}:(x, y, z, t, \theta, \phi) \rightarrow (c_d, \sigma_d)$$

where $(x, y, z)$ is spatial position, $t$ is time, and $(\theta, \phi)$ defines the viewing direction; $c_d$ is RGB color and $\sigma_d$ the corresponding density. The temporal dimension introduces unique challenges, including partial observability, under-constrained geometry, non-rigid deformations, and increased volume of spatio-temporal data.

Representation strategies include:

• Implicit neural fields (e.g., MLPs with time conditioning or canonical-space warping)
• Explicit voxel/tensor grids (e.g., TiNeuVox, D-TensoRF, DeVRF) for memory-efficient, high-throughput inference
• Particle- or point-based approaches (e.g., DAP-NeRF, Point-DynRF) for compositionality and flexibility
• Ray-based and light field networks (e.g., DyLiN/CoDyLiN) for rapid inference and explicit handling of non-rigid and topological scene changes
• Decomposition approaches separating static and dynamic fields, or through tensor factorization for compactness

Hybrid approaches often combine explicit and implicit representations to balance memory usage and fidelity.

2. Motion Modeling and Motion Priors

Reliable motion modeling is a cornerstone of dynamic radiance field methods. Two principal strategies are observed:

• Deformation Fields and Coordinate Warping: Most methods learn a deformation field that maps observed or canonical coordinates to the current configuration, typically via an MLP or a factorized grid (e.g. DeVRF, D-TensoRF, DyLiN). These can be guided by:
  • Per-point canonical mapping
  • Ray deformation (e.g., DyLiN) for holistic ray-based modeling
  • Particle motion models (e.g., DAP-NeRF) with individually parameterized trajectories
• Physics-Driven and Priors-Based Approaches:
  • Sparse/Dense Flow Priors: Fast sparse input methods (e.g., Factorized Motion Fields (Somraj et al., 17 Apr 2024)) leverage cross-view SIFT keypoint matching for robust anchor points and dense RAFT-based optical flow for smooth regions, regularizing the motion field in highly under-constrained, sparse data regimes.
  • Kinematic Fields: Recent approaches (e.g., (Im et al., 19 Jul 2024)) introduce kinematic quantities (velocity, acceleration, jerk) that are jointly learned with radiance fields and regularized using physics-inspired losses to enforce physically plausible motion via Taylor expansion, advective acceleration, and rigidity penalties.

These strategies mitigate the under-constrained nature of dynamic scene reconstruction, especially for monocular or sparsely captured inputs.

3. Rendering Techniques and Hardware Acceleration

Dynamic radiance fields require specialized rendering pipelines to achieve both photorealism and interactive rates:

• Classic Volume Rendering: Most methods build upon the established volumetric rendering integral,

$$C(\mathbf{r}, t) = \int_{t_n}^{t_f} T(\mathbf{r}, s) \sigma(\mathbf{r}(s), t) c(\mathbf{r}(s), t, \mathbf{d}) ds$$

with transmittance $T$ and density $\sigma$ parameterized over time and space.

• Hybrid and Coupled Rendering: Dynamic Mesh-Aware RFs (Qiao et al., 2023) interleave volumetric ray marching and mesh path tracing to handle explicit meshes and NeRF volumes in a physically consistent pipeline. This hybrid approach supports interactive simulation and accurate light/material interplay.
• Feature Video Streams: VideoRF (Wang et al., 2023) introduces a hardware-friendly serialization by mapping 4D radiance fields into temporally consistent streams of 2D feature images, highly amenable to standard video codecs and mobile device video hardware.
• Deferred Shading: VideoRF also employs a deferred shading step, accumulating features per ray and decoding colors via a small, shared MLP, thus achieving real-time rendering on resource-constrained hardware.

These advances enable deployment of complex dynamic radiance field models in real-time and interactive applications, especially on mobile and web platforms.

4. Specialized Data Modalities and Imaging Models

Several methods integrate advanced data modalities or leverage physics-based sensors to enhance reconstruction:

• Time-of-Flight Sensing: TöRF (Attal et al., 2021) incorporates raw continuous-wave ToF camera data, augmenting the radiance field optimization with direct phasor measurements. This extension improves robustness to calibration error, multipath interference, and low reflectance, outperforming depth-map-based approaches especially on challenging dynamic scenes.
• High Dynamic Range (HDR): Multiple methods (HDR-NeRF (Huang et al., 2021), HDR-Plenoxels (Jun-Seong et al., 2022), HDR-HexPlane (Wu et al., 11 Jan 2024)) support HDR scene capture from LDR images of varying exposures. Tone mapping modules model physical imaging pipelines, enabling both HDR and exposure-controllable LDR synthesis with explicit radiance-exposure disentanglement.
• Stylization and Geometry Transfer: GAS-NeRF (Vu et al., 11 Mar 2025) and S-DyRF (Li et al., 10 Mar 2024) demonstrate stylization of both geometry and appearance, using depth maps from style images to transfer 3D structure, and feature-level loss functions, ensuring temporal coherence and physically-plausible, geometry-cognizant stylization even in dynamic settings.

This diversity in sensing and modeling underscores the flexibility of dynamic radiance field frameworks for a range of application domains and sensor inputs.

5. Mesh Reconstruction and Explicit Geometry Extraction

While most dynamic NeRF models focus on view synthesis, recent advances (e.g., Dynamic 2D Gaussians (Zhang et al., 21 Sep 2024)) aim at directly extracting high-quality dynamic mesh sequences for downstream applications. The D-2DGS approach constructs the dynamic object as a collection of planar 2D Gaussians, deformed via sparse control points using linear blend skinning:

$$\mu_j^t = \sum_k w_{jk} \left(R_k^t (\mu_j - p_k) + p_k + T_k^t\right)$$

Object masks from rendered RGB images filter the depth maps, followed by TSDF fusion for mesh generation. This explicit, multi-view–consistent model achieves smoother, more accurate mesh extraction compared to implicit or purely 3D Gaussian models.

6. Novel Directions: Blur, Sparsity, and Physical Regularization

Emerging topics include:

• Blur and Temporal Inconsistency: DyBluRF (Sun et al., 15 Mar 2024) addresses novel view synthesis from motion-blurred monocular video by modeling blur formation as an integration over sharp latent frames, with DCT-based object motion modeling and global cross-time rendering to ensure temporal coherence and sharpness.
• Sparse Observation Regimes: Factorized Motion Fields (Somraj et al., 17 Apr 2024) tackle fast, sparse-input dynamic view synthesis using explicit six-plane factorization for scene flow, regularized by sparse/dense flow priors for tractable and robust optimization in minimal data conditions.
• Physical Consistency: Regularizing Dynamic Radiance Fields with Kinematic Fields (Im et al., 19 Jul 2024) advances physical simulation in dynamic field learning by encoding higher-order motion constraints and enforcing relationships such as advective acceleration and strain rate rigidity, proven effective in empirical reconstruction quality on challenging monocular benchmarks.

These lines of research highlight the field’s progression toward more robust, physically faithful, and data-efficient dynamic scene understanding and synthesis.

7. Applications and Implications

Dynamic radiance field methodologies underpin a wide variety of emerging applications, including:

• Volumetric video and immersive telepresence
• Photorealistic free-viewpoint video for entertainment, sports, and broadcast
• Robotics, AR/VR, and real-time interactive gaming with dynamic, lighting- and geometry-adaptive environments
• Film-quality dynamic scene stylization and geometric editing
• Scientific visualization and dynamic object tracking

Their development is leading to improved generalization, higher-fidelity reconstructions under adverse conditions, and deployment in consumer-grade mobile and edge hardware. The integration with hardware accelerators, physically-inspired modeling, and explicit geometry advances open new possibilities for dynamic scene capture, simulation, and rendering in both academic and industrial settings.