StyleMe3D: Stylize 3D Gaussians with Disentangled Style Encoders!

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The Challenge of Stylizing 3D Scenes

3D Gaussian Splatting (3DGS) has revolutionized photorealistic scene reconstruction, but it faces significant challenges when attempting to produce stylized content for applications like cartoons or games. These challenges stem from fragmented textures, semantic misalignment, and limited adaptability to abstract aesthetic styles.

StyleMe3D addresses these limitations through a holistic framework that enables high-quality stylization of 3D Gaussian scenes. The approach is built on three key insights:

1. Optimizing only RGB attributes preserves geometric integrity during stylization
2. Disentangling low-, medium-, and high-level semantics creates coherent style transfer
3. Scaling from isolated objects to complex scenes is essential for practical applications

The framework introduces four innovative components that work together to create compelling stylized 3D content while maintaining the benefits of Gaussian Splatting's real-time rendering capabilities.

Background and Fundamentals

Style-aware Image Customization

Recent advances in style transfer have produced techniques like StyleShot and IP-Adapter that transfer style from reference images to targets. StyleShot extracts detailed style features using a style-aware encoder with multi-scale patch partitioning to capture both low-level and high-level style cues.

The method divides reference images into non-overlapping patches of three sizes and processes them through dedicated ResBlocks at different depths. Style injection occurs through attention mechanisms:

Attention(Q,Ks,Vs) = softmax(QKs^T/√d)·Vs

Where Q is projected from latent embeddings, and Ks and Vs are keys and values from style embeddings. The process combines attention outputs from text and style embeddings:

f′ = Attention(Q,Kt,Vt) + λ·Attention(Q,Ks,Vs)

With λ balancing the two components to achieve the desired stylization effect.

Score Distillation Sampling for 3D Generation

Text-guided 3D generation has made significant progress through methods like Score Distillation Sampling (SDS). SDS optimizes 3D model parameters θ by distilling gradients from pre-trained diffusion models:

∇θLSDS(ϕ,x=g(θ)) = Et,ϵ[ω(t)(ϵ̂ϕ(zt;y,t)−ϵ)∂x/∂θ]

Here, ϵ̂ϕ(zt;y,t) represents the predicted noise residual from the diffusion model, ϵ is the actual noise, zt is the latent variable at timestep t, and ω(t) is a timestep weighting function.

These techniques have been extended to artistic scene generation and enhanced by latent diffusion models, improving the expressiveness of text-to-3D synthesis for creative applications.

3D Gaussian Splatting

3D Gaussian Splatting represents scenes using spatial Gaussians. Each Gaussian gi is defined by:

• A mean position μi ∈ ℝ³
• A covariance matrix Σi ∈ ℝ³×³
• Opacity αi and view-dependent color ci

The Gaussian's influence at point x is calculated as:

G(x) = e^(-1/2(x-μi)^T·Σi^(-1)·(x-μi))

During rendering, Gaussians are projected to 2D and blended using alpha compositing:

C = ∑(i=1 to n) ci·αi·∏(j=1 to i-1) (1-αj)

This approach enables real-time, differentiable rendering and outperforms NeRF in both speed and memory efficiency, making it ideal for interactive applications.

StyleMe3D: A Multi-Component Framework

Dynamic Style Score Distillation (DSSD)

The DSSD component leverages StyleShot as its backbone, extending it with a style-aware encoder to enhance style representation. Key implementation details include:

1. Fine Timestep Sampling: Using a timestep constant T=1000 with minimum and maximum timesteps set at Tmin=0.02·T and Tmax=0.75·T, dynamically reducing noise intensity during training.

2. Dynamic Guidance Coefficients: Tuning the coefficient Δλ to adapt to dataset scale and style variations, with λmax=20 and Δλ confined within [7.5,20] for the NeRF Synthetic dataset.

3. Multi-stage Optimization: Employing 2800 steps across different guidance modes:

   • Main RGB Loss (Local Mode): Steps 100-600
   • Adaptive Iteration (Global Mode): Steps 1-1000
   • Fixed/Free Global Modes: Steps 1000-1900
   • Local Mode: Steps 1900-2800

This hybrid approach begins with global optimization before transitioning to local refinement, requiring approximately 1800 iterations for SDS loss and 2600 seconds to achieve convergence.

Simultaneously Optimized Scale (SOS)

SOS decouples style details from structural coherence through a two-phase optimization process:

1. VGG Feature Extraction:

   • Style layers: ['r11','r21','r31','r41','r51']
   • Content layer: ['r42']
   • Gram matrix weights: [1e3/64², 1e3/128², 1e3/256², 1e3/512², 1e3/512²]

2. Two-Phase Optimization:

   • Pretraining phase: Triggered when optimize_iteration=10000 and current_iter < 10000, using fixed scale (optimize_size=0.5) and bilinear downsampling
   • Full multi-scale phase: Activates all resize_images scales for comprehensive optimization

Contrastive Style Descriptor (CSD)

CSD enables localized, content-aware texture transfer by deploying a ViT-L style encoder pretrained on the LAION-Styles dataset. This component helps extract distinctive style features while maintaining semantic alignment between content and style.

3D Gaussian Quality Assessment (3DG-QA)

3DG-QA functions as a differentiable aesthetic prior, integrating CLIP-ViT-B with antonymic prompts to evaluate quality metrics:

• Prompts: "Good, Sharp, Colorful" vs "Bad, Blurry, Dull"
• Quality dimensions: quality, sharpness, colorfullness
• Loss function: 1 - (0.4·quality + 0.4·sharpness + 0.2·colorfullness).mean()

This component suppresses artifacts and enhances visual harmony in the final stylized scenes.

Optimization Pathways for Pre-training vs. Post-training. The plot illustrates the optimization pathways for pre-training (blue solid line) and post-training (orange dashed line), highlighting the optimization gap (gray shaded area) between 3D reconstruction and stylization.

Analysis of the Optimization Gap

Misalignment in Optimization Pathways

A fundamental challenge in 3D stylization is the misalignment between pre-training and post-training objectives:

• Pre-training Objective: Focuses on accurate geometric and photometric properties with smooth optimization guided by ground truth data.
• Post-training Objective: Shifts to aesthetic alignment using style-aware guidance with higher uncertainty.
• Disjoint Loss Landscapes: Pre-training minimizes reconstruction errors while stylization incorporates abstract priors from style information.

These distinct optimization pathways can be represented mathematically:

ℒpre = ℒrecon(Gpre(x), xgt)

Where ℒrecon minimizes geometric and photometric errors between predictions and ground truth, and:

ℒpost = ℒstyle(Gpost(x), sref)

Where ℒstyle aligns generated results with a style reference using abstract priors.

The optimization gap between these objectives is quantified as:

Δℒ = |ℒpre - ℒpost|

This gap measures the divergence between loss landscapes, reflecting mismatched optimization goals.

High Uncertainty in Style Information

Style transfer involves inherent uncertainty stemming from:

• Multi-modal Style Representations: Styles lack well-defined ground truth, making optimization less predictable.
• Temporal Instability: Stylization pathways exhibit oscillations due to conflicts between style priors and geometric constraints.

The uncertainty in style optimization can be modeled as variance in style priors:

σstyle² = Var(sref)

Temporal oscillations in optimization are expressed as:

δt = |∇ℒpost,t+1 - ∇ℒpost,t|

This measures instability between consecutive optimization timesteps.

Key Observations and Insights

1. Mismatch in Optimization Curvature:

   • Pre-training has smooth convergence (κpre ≪ κpost)
   • Post-training exhibits oscillatory adjustments
   • Loss landscapes differ fundamentally in curvature

2. Impact of the Optimization Gap:

   • Creates optimization instability: ∇ℒpost ≫ ∇ℒpre
   • Leads to inconsistent stylization due to variance in style priors

3. Bridging Strategies:

   • Style-aware diffusion priors
   • Dynamic style score distillation
   • Progressive style outpainting
   • Regularization: ℒalign = λprior·ℒstyle + λgeo·ℒrecon

These strategies help align optimization pathways and ensure robust stylization while maintaining geometric integrity.

Visual Results

StyleMe3D demonstrates impressive performance across multiple styles applied to various objects. The framework successfully transfers nine distinct styles (sky painting, cartoon, watercolor, fire, cloud, Wukong, drawing, color oil, and sketch) to 3D scenes while preserving their geometric structure.

Table 1: Quantitative comparison with competing methods

The multi-expert version of StyleMe3D further improves upon the basic DSSD implementation:

Table 2: Quantitative comparison between DSSD version and Multi-Expert version

Conclusion

StyleMe3D provides a comprehensive solution to the challenges of 3D stylization by bridging the gap between photorealistic 3D Gaussian Splatting and artistic style transfer. By addressing the fundamental optimization misalignments and employing a multi-component approach with specialized encoders and quality assessment, the framework achieves consistent, high-quality stylization while preserving geometric details.

The method outperforms existing approaches in terms of both quantitative metrics and qualitative visual appeal, enabling applications in gaming, virtual worlds, and digital art that demand both real-time performance and artistic expression.

Related approaches in this emerging field include StylizedGS, SGSST, Gaussian-Splatting-Style, and InstantStyleGaussian.

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