Built on

J. T. Kajiya and B. P. Von Herzen, “Ray tracing volume densities,” ACM SIGGRAPH computer graphics , vol. 18, no. 3, pp. 165–174, 1984
1984
Earlier work this paper cites.
A. Doi and A. Koide, “An efficient method of triangulating equi-valued surfaces by using tetrahedral cells,” IEICE TRANSACTIONS on Information and Systems , vol. 74, no. 1, pp. 214–224, 1991
1991
Earlier work this paper cites.
W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3d surface construction algorithm,” in Seminal graphics: pioneering efforts that shaped the field , 1998, pp. 347–353
1998
Earlier work this paper cites.
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al. , “Shapenet: An information-rich 3d model repository,” arXiv preprint arXiv:1512.03012 , 2015
Original
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” IEEE signal processing magazine , vol. 35, no. 1, pp. 53–65, 2018
2018
Earlier work this paper cites.
K. Chen, C. B. Choy, M. Savva, A. X. Chang, T. Funkhouser, and S. Savarese, “Text2shape: Generating shapes from natural language by learning joint embeddings,” in Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14 . Springer, 2019, pp. 100–116
2019
Earlier work this paper cites.
P. Achlioptas, J. Fan, R. X. D. Hawkins, N. D. Goodman, and L. J. Guibas, “Shapeglot: Learning language for shape differentiation,” CoRR , vol. abs/1905.02925, 2019
Original
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
S. Laine, J. Hellsten, T. Karras, Y. Seol, J. Lehtinen, and T. Aila, “Modular primitives for high-performance differentiable rendering,” ACM Transactions on Graphics , vol. 39, no. 6, 2020
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
D. Kingma, T. Salimans, B. Poole, and J. Ho, “Variational diffusion models,” Advances in neural information processing systems , vol. 34, pp. 21 696–21 707, 2021
2021
Earlier work this paper cites.
J. Hasselgren, J. Munkberg, J. Lehtinen, M. Aittala, and S. Laine, “Appearance-driven automatic 3d model simplification.” in EGSR (DL) , 2021, pp. 85–97
2021
Earlier work this paper cites.
T. Shen, J. Gao, K. Yin, M.-Y. Liu, and S. Fidler, “Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis,” Advances in Neural Information Processing Systems , vol. 34, pp. 6087–6101, 2021
2021
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Earlier work this paper cites.
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv preprint arXiv:2112.10741 , 2021
Original
2021
Earlier work this paper cites.
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” arXiv preprint arXiv:2209.14988 , 2022
Original
2022
Earlier work this paper cites.
A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen, “Point-e: A system for generating 3d point clouds from complex prompts,” arXiv preprint arXiv:2212.08751 , 2022
Original
2022
Earlier work this paper cites.
C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y. Liu, and T.-Y. Lin, “Magic3d: High-resolution text-to-3d content creation,” arXiv preprint arXiv:2211.10440 , 2022
Original
2022
Earlier work this paper cites.
A. Sanghi, H. Chu, J. G. Lambourne, Y. Wang, C.-Y. Cheng, M. Fumero, and K. R. Malekshan, “Clip-forge: Towards zero-shot text-to-shape generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 603–18 613
2022
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Earlier work this paper cites.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125 , vol. 1, no. 2, p. 3, 2022
Original
2022
Earlier work this paper cites.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in neural information processing systems , vol. 35, pp. 36 479–36 494, 2022
2022
Earlier work this paper cites.
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al. , “Laion-5b: An open large-scale dataset for training next generation image-text models,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 278–25 294, 2022
2022
Earlier work this paper cites.
P. Mittal, Y.-C. Cheng, M. Singh, and S. Tulsiani, “AutoSDF: Shape priors for 3d completion, reconstruction and generation,” in CVPR , 2022
2022
Earlier work this paper cites.
Z. Shi, S. Peng, Y. Xu, A. Geiger, Y. Liao, and Y. Shen, “Deep generative models on 3d representations: A survey,” arXiv preprint arXiv:2210.15663 , 2022
Original
2022
Earlier work this paper cites.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598 , 2022
Original
2022
Earlier work this paper cites.
J. Munkberg, J. Hasselgren, T. Shen, J. Gao, W. Chen, A. Evans, T. Müller, and S. Fidler, “Extracting triangular 3d models, materials, and lighting from images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8280–8290
2022
Earlier work this paper cites.
E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al. , “Efficient geometry-aware 3d generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 123–16 133
2022
Earlier work this paper cites.
J. Gao, T. Shen, Z. Wang, W. Chen, K. Yin, D. Li, O. Litany, Z. Gojcic, and S. Fidler, “Get3d: A generative model of high quality 3d textured shapes learned from images,” Advances In Neural Information Processing Systems , vol. 35, pp. 31 841–31 854, 2022
2022
Earlier work this paper cites.
P. Mittal, Y.-C. Cheng, M. Singh, and S. Tulsiani, “Autosdf: Shape priors for 3d completion, reconstruction and generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 306–315
2022
Earlier work this paper cites.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Trans. Graph. , 2022
2022
Earlier work this paper cites.
N. Aigerman, K. Gupta, V. G. Kim, S. Chaudhuri, J. Saito, and T. Groueix, “Neural jacobian fields: Learning intrinsic mappings of arbitrary meshes,” arXiv preprint arXiv:2205.02904 , 2022
Original
2022
Earlier work this paper cites.
H. Jun and A. Nichol, “Shap-e: Generating conditional 3d implicit functions,” arXiv preprint arXiv:2305.02463 , 2023
Original
2023
Earlier work this paper cites.

Similar

J. Li, H. Tan, K. Zhang, Z. Xu, F. Luan, Y. Xu, Y. Hong, K. Sunkavalli, G. Shakhnarovich, and S. Bi, “Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model,” arXiv preprint arXiv:2311.06214 , 2023
Original
2023
Cited alongside, same era.
M. Deitke, D. Schwenk, J. Salvador, L. Weihs, O. Michel, E. VanderBilt, L. Schmidt, K. Ehsani, A. Kembhavi, and A. Farhadi, “Objaverse: A universe of annotated 3d objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 142–13 153
2023
Cited alongside, same era.
M. Deitke, R. Liu, M. Wallingford, H. Ngo, O. Michel, A. Kusupati, A. Fan, C. Laforte, V. Voleti, S. Y. Gadre et al. , “Objaverse-xl: A universe of 10m+ 3d objects,” arXiv preprint arXiv:2307.05663 , 2023
Original
2023
Cited alongside, same era.
B. Zhang, J. Tang, M. Niessner, and P. Wonka, “3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models,” ACM Transactions on Graphics (TOG) , vol. 42, no. 4, pp. 1–16, 2023

Then

Y.-T. Liu, G. Luo, H. Sun, W. Yin, Y.-C. Guo, and S.-H. Zhang, “Pi3d: Efficient text-to-3d generation with pseudo-image diffusion,” arXiv preprint arXiv:2312.09069 , 2023
Original
2023
Later among the works it cites.
M. Zhao, C. Zhao, X. Liang, L. Li, Z. Zhao, Z. Hu, C. Fan, and X. Yu, “Efficientdreamer: High-fidelity and robust 3d creation via orthogonal-view diffusion prior,” arXiv preprint arXiv:2308.13223 , 2023
Original
2023
Later among the works it cites.
Y. Huang, J. Wang, Y. Shi, B. Tang, X. Qi, and L. Zhang, “Dreamtime: An improved optimization strategy for diffusion-guided 3d generation,” in The Twelfth International Conference on Learning Representations , 2023
2023
Later among the works it cites.
R. Chen, Y. Chen, N. Jiao, and K. Jia, “Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation,” arXiv preprint arXiv:2303.13873 , 2023
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

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Reading the bibliography…

2024

A Survey On Text-to-3D Contents Generation In The Wild

Jiang, Chenhan

Understand

3D content creation plays a vital role in various applications, such as gaming, robotics simulation, and virtual reality.

However, the process is labor-intensive and time-consuming, requiring skilled designers to invest considerable effort in creating a single 3D asset.
To address this challenge, text-to-3D generation technologies have emerged as a promising solution for automating 3D creation.
Leveraging the success of large vision language models, these techniques aim to generate 3D content based on textual descriptions.

Built on

J. T. Kajiya and B. P. Von Herzen, “Ray tracing volume densities,” ACM SIGGRAPH computer graphics , vol. 18, no. 3, pp. 165–174, 1984
1984
Earlier work this paper cites.
A. Doi and A. Koide, “An efficient method of triangulating equi-valued surfaces by using tetrahedral cells,” IEICE TRANSACTIONS on Information and Systems , vol. 74, no. 1, pp. 214–224, 1991
1991
Earlier work this paper cites.
W. E. Lorensen and H. E. Cline, “Marching cubes: A high resolution 3d surface construction algorithm,” in Seminal graphics: pioneering efforts that shaped the field , 1998, pp. 347–353
1998
Earlier work this paper cites.
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al. , “Shapenet: An information-rich 3d model repository,” arXiv preprint arXiv:1512.03012 , 2015
Original
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, and A. A. Bharath, “Generative adversarial networks: An overview,” IEEE signal processing magazine , vol. 35, no. 1, pp. 53–65, 2018
2018
Earlier work this paper cites.
K. Chen, C. B. Choy, M. Savva, A. X. Chang, T. Funkhouser, and S. Savarese, “Text2shape: Generating shapes from natural language by learning joint embeddings,” in Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14 . Springer, 2019, pp. 100–116
2019
Earlier work this paper cites.
P. Achlioptas, J. Fan, R. X. D. Hawkins, N. D. Goodman, and L. J. Guibas, “Shapeglot: Learning language for shape differentiation,” CoRR , vol. abs/1905.02925, 2019
Original
2019
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
S. Laine, J. Hellsten, T. Karras, Y. Seol, J. Lehtinen, and T. Aila, “Modular primitives for high-performance differentiable rendering,” ACM Transactions on Graphics , vol. 39, no. 6, 2020
2020
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
D. Kingma, T. Salimans, B. Poole, and J. Ho, “Variational diffusion models,” Advances in neural information processing systems , vol. 34, pp. 21 696–21 707, 2021
2021
Earlier work this paper cites.
J. Hasselgren, J. Munkberg, J. Lehtinen, M. Aittala, and S. Laine, “Appearance-driven automatic 3d model simplification.” in EGSR (DL) , 2021, pp. 85–97
2021
Earlier work this paper cites.
T. Shen, J. Gao, K. Yin, M.-Y. Liu, and S. Fidler, “Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis,” Advances in Neural Information Processing Systems , vol. 34, pp. 6087–6101, 2021
2021
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Earlier work this paper cites.
A. Nichol, P. Dhariwal, A. Ramesh, P. Shyam, P. Mishkin, B. McGrew, I. Sutskever, and M. Chen, “Glide: Towards photorealistic image generation and editing with text-guided diffusion models,” arXiv preprint arXiv:2112.10741 , 2021
Original
2021
Earlier work this paper cites.
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” arXiv preprint arXiv:2209.14988 , 2022
Original
2022
Earlier work this paper cites.
A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen, “Point-e: A system for generating 3d point clouds from complex prompts,” arXiv preprint arXiv:2212.08751 , 2022
Original
2022
Earlier work this paper cites.
C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y. Liu, and T.-Y. Lin, “Magic3d: High-resolution text-to-3d content creation,” arXiv preprint arXiv:2211.10440 , 2022
Original
2022
Earlier work this paper cites.
A. Sanghi, H. Chu, J. G. Lambourne, Y. Wang, C.-Y. Cheng, M. Fumero, and K. R. Malekshan, “Clip-forge: Towards zero-shot text-to-shape generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 603–18 613
2022
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Earlier work this paper cites.
A. Ramesh, P. Dhariwal, A. Nichol, C. Chu, and M. Chen, “Hierarchical text-conditional image generation with clip latents,” arXiv preprint arXiv:2204.06125 , vol. 1, no. 2, p. 3, 2022
Original
2022
Earlier work this paper cites.
C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. L. Denton, K. Ghasemipour, R. Gontijo Lopes, B. Karagol Ayan, T. Salimans et al. , “Photorealistic text-to-image diffusion models with deep language understanding,” Advances in neural information processing systems , vol. 35, pp. 36 479–36 494, 2022
2022
Earlier work this paper cites.
C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortsman et al. , “Laion-5b: An open large-scale dataset for training next generation image-text models,” Advances in Neural Information Processing Systems , vol. 35, pp. 25 278–25 294, 2022
2022
Earlier work this paper cites.
P. Mittal, Y.-C. Cheng, M. Singh, and S. Tulsiani, “AutoSDF: Shape priors for 3d completion, reconstruction and generation,” in CVPR , 2022
2022
Earlier work this paper cites.
Z. Shi, S. Peng, Y. Xu, A. Geiger, Y. Liao, and Y. Shen, “Deep generative models on 3d representations: A survey,” arXiv preprint arXiv:2210.15663 , 2022
Original
2022
Earlier work this paper cites.
J. Ho and T. Salimans, “Classifier-free diffusion guidance,” arXiv preprint arXiv:2207.12598 , 2022
Original
2022
Earlier work this paper cites.
J. Munkberg, J. Hasselgren, T. Shen, J. Gao, W. Chen, A. Evans, T. Müller, and S. Fidler, “Extracting triangular 3d models, materials, and lighting from images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8280–8290
2022
Earlier work this paper cites.
E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al. , “Efficient geometry-aware 3d generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 16 123–16 133
2022
Earlier work this paper cites.
J. Gao, T. Shen, Z. Wang, W. Chen, K. Yin, D. Li, O. Litany, Z. Gojcic, and S. Fidler, “Get3d: A generative model of high quality 3d textured shapes learned from images,” Advances In Neural Information Processing Systems , vol. 35, pp. 31 841–31 854, 2022
2022
Earlier work this paper cites.
P. Mittal, Y.-C. Cheng, M. Singh, and S. Tulsiani, “Autosdf: Shape priors for 3d completion, reconstruction and generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 306–315
2022
Earlier work this paper cites.
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Trans. Graph. , 2022
2022
Earlier work this paper cites.
N. Aigerman, K. Gupta, V. G. Kim, S. Chaudhuri, J. Saito, and T. Groueix, “Neural jacobian fields: Learning intrinsic mappings of arbitrary meshes,” arXiv preprint arXiv:2205.02904 , 2022
Original
2022
Earlier work this paper cites.
H. Jun and A. Nichol, “Shap-e: Generating conditional 3d implicit functions,” arXiv preprint arXiv:2305.02463 , 2023
Original
2023
Earlier work this paper cites.

Similar

J. Li, H. Tan, K. Zhang, Z. Xu, F. Luan, Y. Xu, Y. Hong, K. Sunkavalli, G. Shakhnarovich, and S. Bi, “Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model,” arXiv preprint arXiv:2311.06214 , 2023
Original
2023
Cited alongside, same era.
M. Deitke, D. Schwenk, J. Salvador, L. Weihs, O. Michel, E. VanderBilt, L. Schmidt, K. Ehsani, A. Kembhavi, and A. Farhadi, “Objaverse: A universe of annotated 3d objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 142–13 153
2023
Cited alongside, same era.
M. Deitke, R. Liu, M. Wallingford, H. Ngo, O. Michel, A. Kusupati, A. Fan, C. Laforte, V. Voleti, S. Y. Gadre et al. , “Objaverse-xl: A universe of 10m+ 3d objects,” arXiv preprint arXiv:2307.05663 , 2023
Original
2023
Cited alongside, same era.
B. Zhang, J. Tang, M. Niessner, and P. Wonka, “3dshape2vecset: A 3d shape representation for neural fields and generative diffusion models,” ACM Transactions on Graphics (TOG) , vol. 42, no. 4, pp. 1–16, 2023

Then

Y.-T. Liu, G. Luo, H. Sun, W. Yin, Y.-C. Guo, and S.-H. Zhang, “Pi3d: Efficient text-to-3d generation with pseudo-image diffusion,” arXiv preprint arXiv:2312.09069 , 2023
Original
2023
Later among the works it cites.
M. Zhao, C. Zhao, X. Liang, L. Li, Z. Zhao, Z. Hu, C. Fan, and X. Yu, “Efficientdreamer: High-fidelity and robust 3d creation via orthogonal-view diffusion prior,” arXiv preprint arXiv:2308.13223 , 2023
Original
2023
Later among the works it cites.
Y. Huang, J. Wang, Y. Shi, B. Tang, X. Qi, and L. Zhang, “Dreamtime: An improved optimization strategy for diffusion-guided 3d generation,” in The Twelfth International Conference on Learning Representations , 2023
2023
Later among the works it cites.
R. Chen, Y. Chen, N. Jiao, and K. Jia, “Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation,” arXiv preprint arXiv:2303.13873 , 2023
Original
2023
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2023

Cited alongside, same era.

Y.-C. Cheng, H.-Y. Lee, S. Tulyakov, A. G. Schwing, and L.-Y. Gui, “Sdfusion: Multimodal 3d shape completion, reconstruction, and generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4456–4465

2023

Cited alongside, same era.

Z. Wang, C. Lu, Y. Wang, F. Bao, C. Li, H. Su, and J. Zhu, “Prolificdreamer: High-fidelity and diverse text-to-3d generation with variational score distillation,” arXiv preprint arXiv:2305.16213 , 2023