StyleCity: Large-Scale 3D Urban Scenes Stylization

TL;DR: StyleCity synthesizes style-align urban texture and 360° sky background, while keeping the scene identity intact.

Our proposed novel StyleCity framework can automatically stylize the textured meshes of large-scale urban scenes and generate harmonic omnidirectional sky backgrounds in a controllable manner with input style prompts, i.e., image and text references. Our effective solution has many potential applications, such as making city exploration in Google Earth a more personalized and visually captivating experience.

Abstract

We introduce the first vision-and-text-driven texture stylization system for large-scale urban scenes, StyleCity. Taking an image and text as references, StyleCity stylizes a 3D textured mesh of a large-scale urban scene in a semantics-aware fashion and generates a harmonic omnidirectional sky background. To achieve that, we propose to stylize a neural texture field by transferring 2D vision-and-text priors to 3D globally and locally. During 3D stylization, we progressively scale the planned training views of the input 3D scene at different levels in order to preserve high-quality scene content. We then optimize the scene style globally by adapting the scale of the style image with the scale of the training views. Moreover, we enhance local semantics consistency by the semantics-aware style loss which is crucial for photo-realistic stylization. Besides texture stylization, we further adopt a generative diffusion model to synthesize a style-consistent omnidirectional sky image, which offers a more immersive atmosphere and assists the semantic stylization process. The stylized neural texture field can be baked into an arbitrary-resolution texture, enabling seamless integration into conventional rendering pipelines and significantly easing the virtual production prototyping process.

Framework

StyleCity Framework

Framework overview of StyleCity. Taking style image and text references as input, StyleCity optimizes the neural texture field in a semantics-aware fashion with progressively scaled training views. Synthesized omnidirectional sky enhances style atmosphere and assists semantic style supervision.

Style-Aligned Omnidirectional Sky Synthesis

(a) High-resolution sky synthesis with omnidirectional sampling.
(b) Panoramic sky synthesis comparisons with SyncDiffusion using same seeds and text prompts.

Features:

Multi-scale progressive optimization for large-scale scene
Semantics-aware style transfer
Scale-adaptive style matching
Visual-and-textual style aligned panoramic sky synthesis
Photorealistic or artistic style transfer
No illumination/material required

Results

*Note: all presented videos are compressed and may not fully showcase the details achieved in our method. Please access our high-resolution videos or render your own to fully appreciate the quality of our work.

Photorealistic Time-of-Day Effects

Input Styles

Hallucination of magic times of a day for a city.

Photorealism and Futurism styles

Artistic styles (text-guided editing)

Comparisons

Comparisons among instruct-NeRF2NeRF, ARF, StyleMesh and our proposed method. Our results have plausible texture and background and preserve the scene identity in different style transfer cases.

Comparisons with Google Earth.

Materials

Paper

Supplementary Doc(TBA)

High-res Results(TBA)

Code(TBA)

Data Collector

Citation

			
@inproceedings{chen2024stylecity,
    title={StyleCity: Large-Scale 3D Urban Scenes Stylization with Vision-and-Text Reference via Progressive Optimization},
    author={Chen, Yingshu and Huang, Huajian, Vu, Tuan-Anh and Shum, Ka Chun and Yeung, Sai-Kit},
    booktitle={Preprint},
    year={2024},
    organization={Arxiv}
}

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