The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video
Open accessAbstract
We present Non-Rigid Neural Radiance Fields (NR-NeRF), a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. Our approach takes RGB images of a dynamic scene as input (e.g., from a monocular video recording), and creates a high-quality space-time geometry and appearance representation. We show that a single handheld consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views, e.g. a ‘bullet-time’ video effect. NR-NeRF disentangles the dynamic scene into a canonical volume and its deformation. Scene deformation is implemented as ray bending, where straight rays are deformed non-rigidly. We also propose a novel rigidity network to better constrain rigid regions of the scene, leading to more stable results. The ray bending and rigidity network are trained without explicit supervision. Our formulation enables dense correspondence estimation across views and time, and compelling video editing applications such as motion exaggeration. Our code will be open sourced.
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Cites
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Cited by
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
Reiser, C., Peng, S., Liao, Y., & Geiger, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01407 · Cited by 668
HumanNeRF: Free-viewpoint Rendering of Moving People from Monocular Video
Weng, C.-Y., Curless, B., Srinivasan, P. P., Barron, J. T., & Kemelmacher-Shlizerman, I. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01573 · Cited by 452
Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
Yang, Z., Gao, X., Zhou, W., Jiao, S., Zhang, Y., & Jin, X. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01922 · Cited by 452
HexPlane: A Fast Representation for Dynamic Scenes
Cao, A., & Johnson, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00021 · Cited by 435
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly- Throughs
Turki, H., Ramanan, D., & Satyanarayanan, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01258 · Cited by 423
Metadata
- Authors
- Tretschk, E., Tewari, A., Golyanik, V., Zollhofer, M., Lassner, C., & Theobalt, C.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 12939-12950
- OpenAlex
- W3114462840
- Cited by
- 432
- References
- 94
- FWCI
- 60.72
- Type
- conference paper
- Language
- en
- Open access
- green
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- Tretschk, E., Tewari, A., Golyanik, V., Zollhofer, M., Lassner, C., & Theobalt, C. (2021). Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.01272
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