Efficient Sparse-View 3D Scene Stylization with Hierarchical Neural Representation

מושגי ליבה

A novel coarse-to-fine framework with hierarchical neural representation is proposed to efficiently generate high-quality stylized novel views from sparse input scenes.

תקציר

The paper presents a novel coarse-to-fine framework for sparse-view 3D scene stylization. The key contributions are:

Coarse Geometry Generation:

A simplified NeRF architecture with low-frequency positional encoding is used to capture the coarse scene geometry from sparse input views.
This coarse representation provides a reasonable semantic content of the scene without high-frequency artifacts.

Fine Detail Stylization:

A hierarchical encoding-based neural representation is introduced, which models the high-frequency geometric details as residual values using multi-resolution hash-based feature grids.
The coarse geometric features are combined with the multi-resolution feature grids to assist the MLP in transferring high-frequency information while preserving the semantic content.

Content Annealing:

A new optimization strategy with content strength annealing is designed to better balance the stylization effect and content preservation.
The content loss weight is gradually decreased during training, allowing the model to focus on learning low-frequency details first and then shift to style matching.

The proposed framework effectively handles the stylization of sparse-view 3D scenes, outperforming state-of-the-art methods in both qualitative and quantitative evaluations.

סטטיסטיקה

Our method can generate high-quality stylized novel views from sparse input scenes, outperforming state-of-the-art methods in terms of both consistency and stylization quality.

ציטוטים

"We propose a coarse-to-fine framework for sparse-view 3D scene stylization, which enables efficient and high-quality stylized novel view generation."
"We introduce a hierarchical encoding-based scene representation to model a sparse-view scene from coarse to fine, where the coarse-level representation is first optimized to capture the coarse geometry of a scene from sparse inputs, and then the fine-level representation is directly optimized with the target style to generate the final stylized scene."
"We design a new optimization strategy with content annealing for fine 3D stylized scene generation. Our model can generate accurate semantic content in the early phase of stylization optimization, and later gradually synthesizes high-quality stylized textures that faithfully match the reference style."

תובנות מפתח מזוקקות מ:

Stylizing Sparse-View 3D Scenes with Hierarchical Neural Representation

by Y. Wang,A. G... ב- arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05236.pdf

שאלות מעמיקות

How can the proposed hierarchical representation be extended to handle more complex scene structures, such as dynamic or deformable objects

The proposed hierarchical representation can be extended to handle more complex scene structures, such as dynamic or deformable objects, by incorporating additional information or features that capture the dynamic nature of the objects. For dynamic objects, temporal information can be integrated into the representation to account for changes over time. This can involve encoding the motion or deformation of objects in the scene to ensure consistency across different frames. Techniques like optical flow or spatio-temporal feature extraction can be utilized to capture the dynamic aspects of the scene. By incorporating these dynamic features into the hierarchical representation, the model can better handle complex scene structures with moving or deformable objects.

What are the potential applications of the sparse-view 3D scene stylization technique beyond virtual reality and augmented reality

The sparse-view 3D scene stylization technique has potential applications beyond virtual reality and augmented reality. Some of these applications include:

Digital Art and Design: The ability to stylize 3D scenes with sparse inputs opens up new possibilities for digital artists and designers to create visually appealing and unique artworks.
Entertainment Industry: The technique can be used in the film and animation industry to stylize 3D scenes for movies, TV shows, and video games, adding artistic flair and enhancing visual storytelling.
Architectural Visualization: Architects and designers can use stylized 3D scenes to showcase architectural designs in a more visually engaging and artistic manner.
Product Design: Companies can use stylized 3D scenes to showcase products in a more creative and appealing way, enhancing marketing and advertising efforts.
Education and Training: The technique can be used in educational settings to create interactive and engaging 3D visualizations for teaching complex concepts in subjects like science, engineering, and history.

Can the content annealing strategy be further improved by incorporating perceptual metrics or user preferences to better balance the stylization and content preservation

The content annealing strategy can be further improved by incorporating perceptual metrics or user preferences to better balance the stylization and content preservation. By integrating perceptual metrics such as SSIM, LPIPS, or user preferences into the optimization process, the model can dynamically adjust the weighting between content and style loss based on the perceptual similarity between the stylized output and the reference images. This adaptive approach can ensure that the stylization process prioritizes capturing the essential content details while also achieving the desired artistic style. Additionally, incorporating user feedback or preferences can further refine the stylization process, allowing users to provide input on the balance between content fidelity and stylization effects, leading to more personalized and visually appealing results.