[1] P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas. Learning representations and generative models for 3d point clouds. arXiv preprint arXiv:1707.02392, 2017.
[2] J. An. Model development with vessel, 2023. URL https://www.vessel.ai/. Software available from vessel.ai.
[3] J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan. Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields. In ICCV, 2021.
[4] J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman. Zip-NeRF: Anti-aliased grid-based neural radiance fields. arXiv preprint arXiv:2304.06706, 2021.
[5] M. Bauer, E. Dupont, A. Brock, D. Rosenbaum, J. R. Schwarz, and H. Kim. Spatial functa: Scaling functa to imagenet classification and generation. arXiv preprint arXiv:2302.03130, 2023.
[6] E. Chan, M. Monteiro, P. Kellnhofer, J. Wu, and G. Wetzstein. pi-GAN: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In CVPR, 2021.
[7] E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. D. Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis, T. Karras, and G. Wetzstein. Efficient geometry-aware 3d generative adversarial networks. In CVPR, 2022.
[8] A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu. ShapeNet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012, 2015.
[9] H. Chen, B. He, H. Wang, Y. Ren, S.-N. Lim, and A. Shrivastava. NeRV: Neural representations for videos. In NeurIPS, 2021.
[10] Z. Chen and H. Zhang. Learning implicit fields for generative shape modeling. In CVPR, 2019.
[11] D.-A. Clevert, T. Unterthiner, and S. Hochreiter. Fast and accurate deep network learning by exponential linear units (ELUs). In ICLR, 2016.
[12] Y. Du, K. M. Collins, J. B. Tenenbaum, and V. Sitzmann. Learning signal-agnostic manifolds of neural fields. In NeurIPS, 2021.
[13] E. Dupont, H. Kim, S. M. A. Eslami, D. J. Rezende, and D. Rosenbaum. From data to functa: Your data point is a function and you can treat it like one. In ICML, 2022.
[14] E. Dupont, Y. W. Teh, and A. Doucet. Generative models as distributions of functions. In AISTATS, 2022.
[15] Z. Erkoç, F. Ma, Q. Shan, M. Nießner, and A. Dai. HyperDiffusion: Generating implicit neural fields with weight-space diffusion. In ICCV, 2023.
[16] D. Ha, A. Dai, and Q. V. Le. Hypernetworks. In ICLR, 2016.
[17] M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. In NeurIPS, 2017.
[18] J. Ho, A. Jain, and P. Abbeel. Denoising diffusion probabilistic models. In NeurIPS, 2020.
[19] A. Jaegle, S. Borgeaud, J.-B. Alayrac, C. Doersch, C. Ionescu, D. Ding, S. Koppula, D. Zoran, A. Brock, E. Shelhamer, O. Henaff, M. M. Botvinick, A. Zisserman, O. Vinyals, and J. Carreira. Perceiver IO: A general architecture for structured inputs & outputs. In ICLR, 2022.
[20] S. Jung and M. Keuper. Internalized biases in frechet inception distance. In Workshop on Distribution Shifts, NeurIPS 2021, 2021.
[21] T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila. Progressive growing of gans for improved quality, stability, and variation. In ICLR, 2018.