Style4D: Dynamic Scene Stylization

• Style4D is a research problem focused on stylizing dynamic 4D scenes by transferring a reference artistic style while preserving geometry, motion, and spatiotemporal consistency.
• The approach leverages canonical feature volumes, 4D Gaussian splatting, and zero-shot style transfer methods to ensure temporal coherence and multi-view consistency.
• Benchmark results demonstrate improved image quality, lower LPIPS and RMSE metrics, and strong performance in user studies compared to related methods.

Style4D refers to the stylization of dynamic 4D scenes: given a scene that varies over space and time, the objective is to produce a stylized version that matches a reference artistic style while preserving geometry, motion, temporal stability, and multi-view consistency. In the recent literature, the term names both a broader research problem and a specific benchmark-and-baseline contribution built on 4D Gaussian Splatting, while closely related systems include zero-shot 4D style transfer for dynamic NeRFs and real-time zero-shot stylization for dynamic Gaussian scenes (Chen et al., 26 Aug 2025). The area lies at the intersection of neural style transfer, dynamic scene representation, and 4D rendering, but it is distinct from general text-to-4D generation: some nearby 4D methods improve consistency, photorealism, editability, or animation without introducing an explicit style-conditioning pipeline (Chen et al., 2024).

1. Problem setting and conceptual boundaries

The core Style4D problem is to stylize dynamic scenes so that the result remains coherent from view to view at a fixed time, frame to frame over time, and without re-optimizing for every new style. This formulation makes zero-shot stylization, content preservation, multi-view consistency, temporal coherence, and efficiency central requirements rather than auxiliary desiderata (Liang et al., 2024).

Relative to 2D style transfer, the problem is not limited to matching content and style in a single image. Relative to static 3D stylization, it is complicated by moving or deforming objects, which require consistency across time as well as across viewpoints. The literature identifies several specific difficulties: motion ambiguity, cross-time coherence, memory and computation costs, and instability when style transformations are computed in a time-varying representation rather than a canonical one (Xu et al., 2024).

A useful distinction in this area is between dedicated 4D stylization and adjacent 4D generation. Dedicated stylization methods explicitly transfer a reference style image to an existing 4D scene or a reconstructed dynamic representation. By contrast, mesh-based text-to-4D generation focuses on consistent text-aligned content, animatable meshes, texture editing, and combinational 4D generation, but does not present a dedicated style-control mechanism or a style-conditioning pipeline in the sense of generating 4D content in a specified artistic style (Chen et al., 2024). This boundary is important because many 4D systems improve the prerequisites for stylization—such as geometry preservation, temporal stability, and editability—without themselves solving style transfer.

2. Canonical-space zero-shot stylization in dynamic NeRFs

StyleDyRF introduces the first zero-shot 4D style transfer method for dynamic neural radiance fields. Its central strategy is to represent a dynamic scene in a canonical 3D feature space and learn a linear style transformation there, so that the stylization is consistent across all times (Xu et al., 2024).

The method is organized into four stages: pre-train a dynamic NeRF, distill a canonical feature volume, learn a canonical style transformation, and optionally apply a photorealistic post-processing module. The dynamic NeRF uses a deformation network to map a point at time $t$ into canonical space, sample canonical appearance and geometry features, and then render color and density along rays. The canonical feature volume $V_f$ replaces a naïve full 4D feature volume with a compact 3D representation plus deformation, which directly addresses the memory and computation issue identified for 4D stylization.

The style-transfer stage is based on covariance matching in feature space. Let $F_s$ be the flattened style feature map and $F_c = \pi(V_f)$ the flattened canonical feature volume. The transform $T$ is learned so that the stylized content covariance matches the style covariance:

$T^* = \arg \min_{T} \frac{1}{C_f} \left\| \overline{F}_{cs} \overline{F}_{cs}^T - \overline{F}_s \overline{F}_s^T \right\|_F^2, \qquad \overline{F}_{cs} = T \overline{F}_c.$

Using SVD, the paper derives an exact whitening-coloring form and then replaces explicit full-volume SVD with lightweight neural predictors: a 3D CNN and MLP on the content side, and a 2D CNN and MLP on the style side. This yields a universal linear transformation on the canonical feature volume rather than a per-frame transform, which is the paper’s main mechanism for cross-time consistency.

For photorealistic stylization, StyleDyRF adds a CSPN-based propagation/filtering module guided by the original rendered RGB image. The paper states that this reduces structural distortions introduced by the autoencoder-based stylization pipeline. It also reports smooth multi-style interpolation, because the canonical style transformation is linear and the learned style transformation components and style mean can be interpolated directly.

The experiments use the Nvidia dataset and the DAVIS dataset, with style images drawn from WikiArt. Training requires 10,000 iterations for NeRF pretraining and 2,500 iterations for each of the remaining two stages, for a total of about one day on a single NVIDIA V100. In qualitative comparisons against WCT, LinearWCT, AdaIN, MCCNet, ReReVST, and StyleRF, the paper reports better stylization quality, stronger temporal consistency, stronger multi-view consistency, and better behavior on moving objects. Quantitatively, StyleDyRF-A and especially StyleDyRF-P achieve the best average consistency results, with the lowest LPIPS and RMSE in both short-range and long-range settings (Xu et al., 2024).

3. Embedded 4D Gaussians and real-time zero-shot transfer

4DStyleGaussian extends Gaussian-splatting-based style transfer from static 3D scenes to dynamic 4D scenes. Its stated goal is real-time stylization of arbitrary style references while maintaining reasonable content affinity, multi-view consistency, and temporal coherence (Liang et al., 2024).

The method has two training stages. In the first stage, it replaces per-Gaussian RGB or spherical harmonic color with a learned high-dimensional feature embedding:

$\mathcal{S}'=[\mathcal{X}',s',r',\sigma,\mathcal{F}], \qquad \mathcal{F} \in \mathbb{R}^{32}.$

These features are rendered by differentiable splatting and then mapped by two MLPs to a color map $C$ and a feature map $F$. In the second stage, the method computes a style transform in feature space and applies it consistently across Gaussian features. The transform follows the familiar whitening/coloring structure,

$T = T_s T_c,$

with $V_f$0 a whitening transform and $V_f$1 a coloring transform, but predicts the transforms with lightweight networks rather than solving an explicit test-time SVD for each case.

A major design choice is the use of a reversible neural network for feature embedding. The paper’s argument is that conventional pre-trained encoder-decoder pipelines, especially VGG-based ones, can discard fine content details and introduce artifacts because they were designed for classification. The reversible network is used to reduce content loss, blur from downsampling, and artifacts introduced by conventional VGG-based feature distillation. Stylized Gaussian features are decoded back to RGB through the reverse process, producing zero-shot stylization without per-style optimization.

Because Gaussian Splatting can still produce elongated structures, splotchy regions, and visibility or ordering distortions, the method adds a convolutional spatial propagation network:

$V_f$2

This acts as a refinement module to improve photorealism and reduce artifacts. The paper’s ablation states that removing the propagation module increases visible artifacts caused by anisotropic or splotchy Gaussians.

The experimental setup uses DyNeRF with 21 static cameras at 30 FPS, with 20 views for training and 1 view for testing. WikiArt provides the style images. Training uses one RTX 3090Ti, with 3000 iterations for coarse 3D Gaussian optimization, 1500 iterations for fine 4D Gaussian optimization, and 1500 iterations for style transfer training, for a total training time of about 6 hours. Inference is reported at 0.037 s per frame, which the paper identifies as near real time (Liang et al., 2024).

Quantitatively, the method improves short-range and long-range consistency measured by LPIPS and RMSE between stylized nearby views aligned with optical flow. For short-range consistency, the reported mean results are RMSE 0.015 and LPIPS 0.012 for the method, compared with RMSE 0.031 and LPIPS 0.013 for StyleGaussian, RMSE 0.038 and LPIPS 0.018 for StyleRF, and RMSE 0.046 and LPIPS 0.022 for 4D-DGSO. For long-range consistency, the method reports RMSE 0.020 and LPIPS 0.018, again better than the compared baselines. Efficiency comparisons show 0.037 s inference for arbitrary styles, compared with 30.22 s for StyleRF, 0.155 s for ReReVST, 0.231 s for CAP-VSTNet, and 0.189 s for StyleGaussian (Liang et al., 2024).

4. Style4D-Bench and the Style4D baseline

Style4D-Bench defines 4D stylization as a benchmarked research problem and pairs that benchmark with Style4D, a strong baseline built on 4D Gaussian Splatting. The benchmark is designed to standardize evaluation and to assess not only how stylized individual frames appear, but also how well a method preserves structure, remains stable across time, and stays consistent across viewpoints (Chen et al., 26 Aug 2025).

The benchmark uses the Neu3D dataset, with six real-world dynamic scenes: flame_steak, sear_steak, flame_salmon_1, coffee_martini, cook_spinach, and cut_roasted_beef. Each scene has 15–20 static cameras, 300-frame sequences, and resolution 1352 × 1014. The protocol decomposes evaluation into frame-wise quality, temporal or spatiotemporal consistency, and style-versus-content trade-off. It measures six dimensions and 12 metrics in total. The metrics are grouped into spatial or imaging quality (UIQM, CLIPIQA+, MUSIQ), aesthetic quality (Qalign, MUSIQ-PAQ2PIQ), temporal or spatiotemporal consistency (DISTS, warp loss using RAFT optical flow, DINO feature similarity for subject consistency), stylization quality (CKDN, LPIPS against the style image), and content consistency (SSIM, LPIPS against the content or reference scene). A user study with 34 participants evaluates both single frames and long videos.

The Style4D method itself is geometry-first. It first reconstructs scene geometry using a basic 4DGS representation and a deformation field network $V_f$3, then learns stylization on top of this backbone. The framework has three components: Basic 4DGS representation, Style Gaussian Representation, and Holistic Geometry-Preserved Style Transfer (HGST). The Style Gaussian Representation augments each Gaussian with a style-aware feature predicted by a lightweight per-Gaussian MLP, rather than relying only on static per-Gaussian color. The stylized rendering rule is given as

$V_f$4

where $V_f$5 is the style-driven color increment predicted from ray–Gaussian intersection depth and time. The paper emphasizes that directly mapping intersection coordinates to color or opacity can overfit; using depth and time is meant to provide finer control while preserving geometry.

HGST supplies high-quality stylized supervision frames for training the Style Gaussian Representation. It is based on an encoder–transformer–decoder architecture and combines Multi-Channel Correlation-style feature fusion, contrastive coherence learning, and content-preserving regularization. The local coherence term is

$V_f$6

and the content-preserving term is

$V_f$7

These are combined into

$V_f$8

The full HGST objective is

$V_f$9

with appendix weights $F_s$0, $F_s$1, $F_s$2, $F_s$3, and $F_s$4.

Across three reported benchmark scenes, the paper states that Style4D generally achieves the best results on imaging quality, aesthetic quality, temporal consistency, subject consistency, content consistency, and style alignment. Example values include UIQM 1.9290, MUSIQ 53.4681, Qalign 3.2072, DISTS 0.0112, and SSIM 0.7771 on cook_spinach; Qalign 3.6030 and MUSIQ-PAQ2PIQ 63.5178 on flame_salmon_1; and DISTS 0.0108, warp loss 0.0066, DINO 0.9564, and SSIM 0.7503 on sear_steak. The paper also reports better style loss than 4DStyleGaussian, with 0.005687 versus 0.006816 on sear_steak, 0.006123 versus 0.008329 on cook_spinach, and 0.006364 versus 0.007387 on flame_salmon_1. In user studies, Style4D receives 61.76% of stylization votes and 70.58% of image-quality votes for single frames, and much higher preference than baselines for videos across stylization quality, video quality, and spatiotemporal consistency, often around 75–88% depending on the comparison (Chen et al., 26 Aug 2025).

5. Representation choices and consistency mechanisms

The methods associated with Style4D differ chiefly in how they represent dynamic scenes and where they impose consistency. A concise comparison is useful.

Method	Scene representation	Main consistency mechanism
StyleDyRF	Canonical feature volume with dynamic NeRF deformation	Canonical style transformation on covariance statistics
4DStyleGaussian	Embedded 4D Gaussian Splatting	Global linear feature transform plus reversible embedding
Style4D	4DGS with Style Gaussian Representation	HGST supervision and temporally aware per-Gaussian modulation
CT4D	Explicit animatable triangle mesh	Uniform regional driving and rigidity regulation
4Real	Canonical deformable 3D Gaussian scene	Per-frame deformation, temporal deformation, and dual SDS

Two recurring design patterns are especially notable. First, dedicated 4D stylization methods tend to avoid frame-wise style transfer in image space and instead stylize a scene-level representation: canonical feature volumes in StyleDyRF, embedded Gaussians in 4DStyleGaussian, and stylized Gaussian attributes in Style4D (Xu et al., 2024). Second, consistency is usually enforced through explicit architectural or regularization choices rather than by relying on a perceptual loss alone. StyleDyRF uses a universal linear transform in canonical space; 4DStyleGaussian applies a global linear 4D style transformation matrix and adds a photorealistic propagation module; Style4D introduces contrastive coherence learning and content-preserving regularization in HGST; CT4D partitions meshes into regions, uses uniform driving within each area, and constrains animation with rigidity regulation to maintain cross-region surface continuity (Liang et al., 2024).

This suggests a methodological split within the field. One branch emphasizes zero-shot global transforms on a stable scene representation, as in StyleDyRF and 4DStyleGaussian. Another emphasizes scene-specific stylization supervision on top of a reliable geometric backbone, as in Style4D. The first branch naturally supports arbitrary unseen style images without per-style training; the second prioritizes the balance between style strength, structure preservation, multi-view consistency, and temporal stability within a benchmarked evaluation setting (Chen et al., 26 Aug 2025).

6. Relation to neighboring 4D generation work and unresolved issues

The broader 4D literature supplies important foundations for Style4D even when it is not itself a stylization literature. CT4D replaces dynamic NeRF or 3D Gaussian Splatting with an explicit animatable triangle mesh, using a Generate–Refine–Animate pipeline, k-means clustering on mesh vertices, pseudo-skinning weights, handle points, uniform per-region motion, and an ARAP-style rigidity regularization. Its main claims are better surface continuity, better interframe consistency, better global geometry preservation, and easier editing and composition, together with support for texture editing and combinational 4D generation. At the same time, the paper explicitly states that stylization or style-controlled generation is not a core claim and that it is only indirectly relevant to Style4D-style questions (Chen et al., 2024).

4Real occupies a different adjacent position. It addresses photorealistic text-to-4D scene generation by turning the problem into a video-first reconstruction pipeline: text prompt to reference video, reference video to freeze-time video, freeze-time video to canonical 3D Gaussian scene, and then temporal deformation. It introduces per-frame deformation to absorb inconsistencies in the freeze-time video, temporal deformation to model actual motion, and multi-view plus temporal SDS regularization. The paper argues that using video diffusion models trained on real-world data rather than multi-view generative models trained on synthetic object datasets yields more realistic scene-level 4D content with improved photorealism and structural integrity (Yu et al., 2024). A plausible implication is that such photorealistic backbones can be combined with explicit stylization modules, although that integration is not presented directly.

The field also has several recurrent limitations. Style4D states that training is time-consuming, that the current system is mainly for a fixed style per scene, and that region-specific stylization remains an open challenge (Chen et al., 26 Aug 2025). 4DStyleGaussian acknowledges residual elongated and splotchy artifacts and requires an extra propagation module to clean them up (Liang et al., 2024). StyleDyRF depends on the quality of the pre-trained dynamic NeRF, the canonical-space deformation prior, and an additional CSPN post-processing module for photorealistic transfer (Xu et al., 2024). 4Real depends on the quality of the video generator, faces camera pose errors and fast motion in dynamic reconstruction, does not produce high-quality meshes, and still takes over an hour for a roughly 2-second scene (Yu et al., 2024).

Taken together, these works establish 4D stylization as more than an ad hoc extension of 2D or static 3D style transfer. The benchmarking effort in Style4D-Bench formalizes the task; StyleDyRF and 4DStyleGaussian show that zero-shot stylization can be made temporally and spatially coherent through canonical or embedded scene representations; and adjacent 4D generation systems clarify which properties—photorealism, geometry preservation, animation control, texture editability—are prerequisites for future style-aware dynamic scene synthesis.