Neural 3D Strokes: Creating Stylized 3D Scenes with Vectorized 3D Strokes
Core Concepts
新しい技術で、マルチビュー2D画像からスタイリッシュな3Dシーンを生成する方法を提案します。
Abstract
Introduction:
Neural network-based style transfer revolutionizes artistic image creation.
Extending to 3D scenes is challenging due to complex geometries.
Existing methods like NeRF stylize color aspects but lack geometric changes.
Methodology:
Neural 3D Strokes simulates human artwork painting process with vector strokes.
Differentiable rendering of 3D strokes using stroke fields and composition techniques.
Training scheme includes error field, stroke initialization, and update strategies.
Experiments:
Tested on synthetic objects and face-forwarding scenes datasets.
Various types of vectorized 3D strokes used for scene reconstruction.
Comparison with other image-to-painting methods shows detailed results.
Applications:
Color transfer achieved by fine-tuning color parameters while fixing geometry.
Text-driven scene drawing demonstrated with zero-shot generation framework.
Neural 3D Strokes
Stats
NeRF has become popular due to its differentiability and ease of optimization.
Neural style transfer in both 2D and 3D manipulates pixels or voxels for artistic effects.
Quotes
"Our method represents the scene as vectorized 3D strokes, mimicking human painting during scene reconstruction process."
"Color transfer effects achieved by fixing geometry and fine-tuning color parameters."