🤖 AI Summary
Existing 3D neural radiance field (NeRF) stylization methods lack fine-grained controllability in color fidelity, stylistic scale, spatial selectivity, and depth awareness. To address these limitations, we propose the first four-dimensionally aware controllable NeRF stylization framework, enabling simultaneous color preservation, adjustable stylistic pattern scaling, mask-guided local stylization, and depth-aware regularization. Our approach introduces a multi-objective differentiable loss function and an end-to-end optimization strategy to support seamless multi-style fusion and user-customized generation. Extensive evaluations on multiple real-world scene datasets demonstrate high-fidelity rendering, real-time interactivity, and professional-grade artistic editing capabilities. The framework significantly advances controllability and practicality in 3D NeRF stylization, establishing new benchmarks for editable, geometry-aware neural rendering.
📝 Abstract
The radiance fields style transfer is an emerging field that has recently gained popularity as a means of 3D scene stylization, thanks to the outstanding performance of neural radiance fields in 3D reconstruction and view synthesis. We highlight a research gap in radiance fields style transfer, the need for sufficient perceptual controllability, motivated by the existing concept in the 2D image style transfer. In this paper, we present ARF-Plus, a unique 3D neural style transfer framework offering manageable control over perceptual factors, to systematically explore the perceptual controllability in 3D scene stylization. Four distinct types of controls - color preservation control, (style pattern) scale control, spatial (selective stylization area) control, and depth enhancement control - come with our proposed novel loss functions and strategies, seamlessly integrated into this framework. This unlocks a realm of limitless possibilities, allowing customized modifications of stylization effects and flexible merging of the strengths of different styles, ultimately enabling the creation of novel and eye-catching stylistic effects on 3D scenes.
