The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis
Open accessAbstract
We present DietNeRF, a 3D neural scene representation estimated from a few images. Neural Radiance Fields (NeRF) learn a continuous volumetric representation of a scene through multi-view consistency, and can be rendered from novel viewpoints by ray casting. While NeRF has an impressive ability to reconstruct geometry and fine details given many images, up to 100 for challenging 360° scenes, it often finds a degenerate solution to its image reconstruction objective when only a few input views are available. To improve few-shot quality, we propose DietNeRF. We introduce an auxiliary semantic consistency loss that encourages realistic renderings at novel poses. DietNeRF is trained on individual scenes to (1) correctly render given input views from the same pose, and (2) match high-level semantic attributes across different, random poses. Our semantic loss allows us to supervise DietNeRF from arbitrary poses. We extract these semantics using a pre-trained visual encoder such as CLIP, a Vision Transformer trained on hundreds of millions of diverse single-view, 2D photographs mined from the web with natural language supervision. In experiments, DietNeRF improves the perceptual quality of few-shot view synthesis when learned from scratch, can render novel views with as few as one observed image when pre-trained on a multi-view dataset, and produces plausible completions of completely unobserved regions. Our project website is available at https://www.ajayj.com/dietnerf.
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Cited by
RegNeRF: Regularizing Neural Radiance Fields for View Synthesis from Sparse Inputs
Niemeyer, M., Barron, J. T., Mildenhall, B., Sajjadi, M. S. M., Geiger, A., & Radwan, N. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00540 · Cited by 609
Extract Free Dense Labels from CLIP
Zhou, C., Loy, C. C., & Dai, B. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19815-1_40 · Cited by 513
MotionDiffuse: Text-Driven Human Motion Generation with Diffusion Model
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Sajjadi, M. S. M., Meyer, H., Pot, E., Bergmann, U., Greff, K., Radwan, N., Vora, S., Lucic, M., Duckworth, D., Dosovitskiy, A., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00613 · Cited by 143
DreamSim: Learning New Dimensions of Human Visual Similarity using Synthetic Data
Fu, S., Tamir, N., Sundaram, S., Chai, L., Zhang, R., Dekel, T., & Isola, P. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-2208 · Cited by 108
Metadata
- Authors
- Jain, A., Tancik, M., & Abbeel, P.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 5865-5874
- OpenAlex
- W3139743318
- Cited by
- 463
- References
- 11
- FWCI
- 54.80
- Type
- conference paper
- Language
- en
- Open access
- green
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