ARF: Artistic Radiance Fields

Zhang, Kai; Kolkin, Nick; Bi, Sai; Luan, Fujun; Xu, Zexiang; Shechtman, Eli; Snavely, Noah

doi:10.1007/978-3-031-19821-2_41

Cited by 93 publications

(41 citation statements)

References 42 publications

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“…Using neural networks to implicitly represent 3D scenes [29,36,48,49,52,57] has drawn much recent attention. NeRF [32] and its variants [2,3,30,34,53,54,61,66] have achieved impressive results on novel view synthesis [8,57,63] and have many applications ncluding 3D reconstruction [28,52,64,69,72], semantic segmentation [19,44,71], generative model [5,6,9,35,46], 3D content creation [1,17,37,42,55,65].…”

Section: Related Workmentioning

confidence: 99%

HexPlane: A Fast Representation for Dynamic Scenes

Cao¹,

Johnson²

2023

Preprint

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Modeling and re-rendering dynamic 3D scenes is a challenging task in 3D vision. Prior approaches build on NeRF and rely on implicit representations. This is slow since it requires many MLP evaluations, constraining real-world applications. We show that dynamic 3D scenes can be explicitly represented by six planes of learned features, leading to an elegant solution we call HexPlane. A HexPlane computes features for points in spacetime by fusing vectors extracted from each plane, which is highly efficient. Pairing a HexPlane with a tiny MLP to regress output colors and training via volume rendering gives impressive results for novel view synthesis on dynamic scenes, matching the image quality of prior work but reducing training time by more than 100×. Extensive ablations confirm our HexPlane design and show that it is robust to different feature fusion mechanisms, coordinate systems, and decoding mechanisms. HexPlanes are a simple and effective solution for representing 4D volumes, and we hope they can broadly contribute to modeling spacetime for dynamic 3D scenes. 1

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Section: Related Workmentioning

confidence: 99%

HexPlane: A Fast Representation for Dynamic Scenes

Cao¹,

Johnson²

2023

Preprint

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“…While the above works focus on rendering realistic novel view images or reconstructing accurate 3D geometry, some works currently aim to exploit 2D pretrained models to learn a priori knowledge for the neural field. Examples include creating 3D objects driven by text [28,54,71], animating NeRF by audio signals [18,40], and stylizing scenes [26,45,81]. Unlike the previous works, in this paper, we focus on utilizing semantic image synthesis models.…”

Section: Related Workmentioning

confidence: 99%

Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask

Zhang¹,

Peng²,

Chen³

et al. 2023

Preprint

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“…[37] also constructs a point cloud for stylization but it mainly focuses on monocular images. Instead, [6,8,11,22,39,63] use NeRF [36] as the 3D representation which can reconstruct scene geometry more faithfully. [6] is a photorealistic style transfer method that can only transfer the color tone of style images.…”

Section: Related Workmentioning

confidence: 99%

“…[6] is a photorealistic style transfer method that can only transfer the color tone of style images. [39,63] achieve 3D style transfer via optimization and can produce visually high-quality stylization, but they require a time-consuming optimization procedure for every reference style. [11,22] employ latent codes to represent a set of pre-defined styles, but cannot generalize to unseen styles.…”

Section: Related Workmentioning

confidence: 99%

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StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields

Liu¹,

Zhan²,

Chen³

et al. 2023

Preprint

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3D style transfer aims to render stylized novel views of a 3D scene with multi-view consistency. However, most existing work suffers from a three-way dilemma over accurate geometry reconstruction, high-quality stylization, and being generalizable to arbitrary new styles. We propose StyleRF (Style Radiance Fields), an innovative 3D style transfer technique that resolves the three-way dilemma by performing style transformation within the feature space of a radiance field. StyleRF employs an explicit grid of high-level features to represent 3D scenes, with which highfidelity geometry can be reliably restored via volume rendering. In addition, it transforms the grid features according to the reference style which directly leads to highquality zero-shot style transfer. StyleRF consists of two innovative designs. The first is sampling-invariant content transformation that makes the transformation invari-

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ARF: Artistic Radiance Fields

Cited by 93 publications

References 42 publications

HexPlane: A Fast Representation for Dynamic Scenes

HexPlane: A Fast Representation for Dynamic Scenes

Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask

StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields

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