Fetching the paper…
Reading the bibliography…
While recent work on text-conditional 3D object generation has shown promising results, the state-of-the-art methods typically require multiple GPU-hours to produce a single sample.
Generative modeling by estimating gradients of the data distribution
Song, Y. and Ermon, S · 1907
Earlier work this paper cites.
Marching cubes: A high resolution 3d surface construction algorithm
Lorensen, W. E. and Cline, H. E · 1987
Earlier work this paper cites.
Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2006
Earlier work this paper cites.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Printing insecurity? the security implications of 3d-printing of weapons
Walther, G · 2014
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., and Ganguli, S · 2015
Earlier work this paper cites.
3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
Earlier work this paper cites.
3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
Choy, C. B., Xu, D., Gwak, J., Chen, K., and Savarese, S · 2016
Earlier work this paper cites.
A point set generation network for 3d object reconstruction from a single image
Fan, H., Su, H., and Guibas, L · 2016
Earlier work this paper cites.
The risks of revolution: Ethical dilemmas in 3d printing from a us perspective
Neely, E. L · 2016
Earlier work this paper cites.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Earlier work this paper cites.
Evaluation of the use of 3D printing and imaging to create working replica keys
Straub, J. and Kerlin, S · 2016
Earlier work this paper cites.
Learning representations and generative models for 3d point clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L · 2017
Earlier work this paper cites.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C. R., Yi, L., Su, H., and Guibas, L. J · 2017
Earlier work this paper cites.
Neural discrete representation learning
van den Oord, A., Vinyals, O., and Kavukcuoglu, K · 2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
Earlier work this paper cites.
Text2shape: Generating shapes from natural language by learning joint embeddings
Chen, K., Choy, C. B., Savva, M., Chang, A. X., Funkhouser, T., and Savarese, S · 2018
Earlier work this paper cites.
Blender - a 3D modelling and rendering package
Community, B. O · 2018
Earlier work this paper cites.
Atlasnet: A papier-mâché approach to learning 3d surface generation
Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., and Aubry, M · 2018
Earlier work this paper cites.
Multi-view to novel view: Synthesizing novel views with self-learned confidence
Sun, S.-H., Huh, M., Liao, Y.-H., Zhang, N., and Lim, J. J · 2018
Earlier work this paper cites.
Pixel2mesh: Generating 3d mesh models from single rgb images
Wang, N., Zhang, Y., Li, Z., Fu, Y., Liu, W., and Jiang, Y.-G · 2018
Earlier work this paper cites.
Gkioxari, G., Malik, J., and Johnson, J · 2019
Earlier work this paper cites.
Structurenet: Hierarchical graph networks for 3d shape generation
Mo, K., Guerrero, P., Yi, L., Su, H., Wonka, P., Mitra, N., and Guibas, L. J · 2019
Earlier work this paper cites.
Pointflow: 3d point cloud generation with continuous normalizing flows
Yang, G., Huang, X., Hao, Z., Liu, M.-Y., Belongie, S., and Hariharan, B · 2019
Cited alongside, same era.
Learning gradient fields for shape generation
Cai, R., Yang, G., Averbuch-Elor, H., Hao, Z., Belongie, S., Snavely, N., and Hariharan, B · 2020
Cited alongside, same era.
pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
Chan, E. R., Monteiro, M., Kellnhofer, P., Wu, J., and Wetzstein, G · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
Cited alongside, same era.
Modular primitives for high-performance differentiable rendering
Zero-shot text-to-image generation
Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., and Sutskever, I · 2021
Later among the works it cites.
High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2021
Later among the works it cites.
Image super-resolution via iterative refinement
Saharia, C., Ho, J., Chan, W., Salimans, T., Fleet, D. J., and Norouzi, M · 2021
Later among the works it cites.
Clip-forge: Towards zero-shot text-to-shape generation
Sanghi, A., Chu, H., Lambourne, J. G., Wang, Y., Cheng, C.-Y., Fumero, M., and Malekshan, K. R · 2021
Later among the works it cites.
ediff-i: Text-to-image diffusion models with an ensemble of expert denoisers, 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Laine, S., Hellsten, J., Karras, T., Seol, Y., Lehtinen, J., and Aila, T · 2020
Cited alongside, same era.
Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 2020
Cited alongside, same era.
Graf: Generative radiance fields for 3d-aware image synthesis
Schwarz, K., Liao, Y., Niemeyer, M., and Geiger, A · 2020
Cited alongside, same era.
Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
Cited alongside, same era.
pixelnerf: Neural radiance fields from one or few images
Yu, A., Ye, V., Tancik, M., and Kanazawa, A · 2020
Cited alongside, same era.
Efficient geometry-aware 3d generative adversarial networks
Chan, E. R., Lin, C. Z., Chan, M. A., Nagano, K., Pan, B., Mello, S. D., Gallo, O., Guibas, L., Tremblay, J., Khamis, S., Karras, T., and Wetzstein, G · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P. and Nichol, A · 2021
Cited alongside, same era.
Cogview: Mastering text-to-image generation via transformers
Ding, M., Yang, Z., Hong, W., Zheng, W., Zhou, C., Yin, D., Lin, J., Zou, X., Shao, Z., Yang, H., and Tang, J · 2021
Cited alongside, same era.
Balaji, Y., Nah, S., Huang, X., Vahdat, A., Song, J., Kreis, K., Aittala, M., Aila, T., Laine, S., Catanzaro, B., Karras, T., and Liu, M.-Y · 2022
Closest in time.
Gaudi: A neural architect for immersive 3d scene generation
Bautista, M. A., Guo, P., Abnar, S., Talbott, W., Toshev, A., Chen, Z., Dinh, L., Zhai, S., Goh, H., Ulbricht, D., Dehghan, A., and Susskind, J · 2022
Closest in time.
Feng, Z., Zhang, Z., Yu, X., Fang, Y., Li, L., Chen, X., Lu, Y., Liu, J., Yin, W., Feng, S., Sun, Y., Tian, H., Wu, H., and Wang, H · 2022
Closest in time.
Shapecrafter: A recursive text-conditioned 3d shape generation model
Fu, R., Zhan, X., Chen, Y., Ritchie, D., and Sridhar, S · 2022
Closest in time.
Make-a-scene: Scene-based text-to-image generation with human priors
Gafni, O., Polyak, A., Ashual, O., Sheynin, S., Parikh, D., and Taigman, Y · 2022
Closest in time.
Get3d: A generative model of high quality 3d textured shapes learned from images
Gao, J., Shen, T., Wang, Z., Chen, W., Yin, K., Li, D., Litany, O., Gojcic, Z., and Fidler, S · 2022
Closest in time.
Cogvideo: Large-scale pretraining for text-to-video generation via transformers
Hong, W., Ding, M., Zheng, W., Liu, X., and Tang, J · 2022
Closest in time.
Elucidating the design space of diffusion-based generative models
Karras, T., Aittala, M., Aila, T., and Laine, S · 2022
Closest in time.
Clip-mesh: Generating textured meshes from text using pretrained image-text models
Khalid, N. M., Xie, T., Belilovsky, E., and Popa, T · 2022
Closest in time.
Towards implicit text-guided 3d shape generation
Liu, Z., Wang, Y., Qi, X., and Fu, C.-W · 2022
Closest in time.
Dall·e 2 preview - risks and limitations
Mishkin, P., Ahmad, L., Brundage, M., Krueger, G., and Sastry, G · 2022
Closest in time.
Autosdf: Shape priors for 3d completion, reconstruction and generation
Mittal, P., Cheng, Y.-C., Singh, M., and Tulsiani, S · 2022
Closest in time.
Dreamfusion: Text-to-3d using 2d diffusion
Poole, B., Jain, A., Barron, J. T., and Mildenhall, B · 2022
Closest in time.
Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
Closest in time.
Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
Closest in time.
Textcraft: Zero-shot generation of high-fidelity and diverse shapes from text
Sanghi, A., Fu, R., Liu, V., Willis, K., Shayani, H., Khasahmadi, A. H., Sridhar, S., and Ritchie, D · 2022
Closest in time.
Make-a-video: Text-to-video generation without text-video data
Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., Parikh, D., Gupta, S., and Taigman, Y · 2022
Closest in time.
Novel view synthesis with diffusion models
Watson, D., Chan, W., Martin-Brualla, R., Ho, J., Tagliasacchi, A., and Norouzi, M · 2022
Closest in time.
Scaling autoregressive models for content-rich text-to-image generation
Yu, J., Xu, Y., Koh, J. Y., Luong, T., Baid, G., Wang, Z., Vasudevan, V., Ku, A., Yang, Y., Ayan, B. K., Hutchinson, B., Han, W., Parekh, Z., Li, X., Zhang, H., Baldridge, J., and Wu, Y · 2022
Closest in time.
Lion: Latent point diffusion models for 3d shape generation
Zeng, X., Vahdat, A., Williams, F., Gojcic, Z., Litany, O., Fidler, S., and Kreis, K · 2022
Closest in time.