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About this paper
Hongbin Xu, Weitao Chen, Feng Xiao, Baigui Sun, Wenxiong Kang
StyleDyRF introduces a zero-shot 4D style transfer framework for dynamic Neural Radiance Fields, enabling the application of arbitrary visual styles to synthesized novel views of dynamic 3D scenes over time. The approach achieves enhanced multi-view and cross-time consistency, outperforming existing 2D, 3D, and video style transfer methods.
Key insights
- Utilizing a canonical 3D space and a Canonical Feature Volume (CFV) allows for consistent representation of dynamic objects over time.
- Performing style transformation directly on the canonical latent feature space, rather than on individual frames or volumes, is crucial for achieving both multi-view and cross-time consistency in dynamic scenes.
- Data-driven estimation of whitening and coloring matrices applied to 2D rendered feature maps significantly reduces computational complexity compared to direct 3D volume SVD operations.
Summary by alphaXiv.
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