3D Generative Techniques and Visualization: A Brief Survey
DOI:
https://doi.org/10.15837/ijccc.2025.3.7021Keywords:
synthetic data generation, 3D generative methods, 3D visualization, evaluation metrics for synthetic dataAbstract
The quality and quantity of data in the datasets is the key factor in producing accurate results for artificial intelligence applications. Real data is costly both from the time of gathering and from the labeling point of view. Moreover, the data property problem, the anonymization, and the representativeness are important factors that limit the dimension of the real datasets, making Synthetic Data Generation (SDG) the only alternative to produce large, high-quality datasets. The process of creating 3D synthetic data involves several steps, such as choosing the 3D model tool, creating the 3D model, applying texture and materials, setting up lighting, defining camera parameters, rendering the scene, augmenting data, adding depth and annotations, compiling the dataset, and performing validation and testing. Our paper explores the current landscape of 3D SDG, including generative methods, metrics, areas of application, existing packages to generate 3D data, and visualization of the generated data. The main objective is to focus on the specifics of 3D data, with an emphasis on the very recent state-of-the-art generative adversarial network techniques and assessment methods. We also discuss limitations of current 3D data generation techniques, challenges, and promising research directions.
References
K. Man and J. Chahl, “A review of synthetic image data and its use in computer vision,” Journal of Imaging, vol. 8, no. 11, p. 310, 2022.
G. Pei, J. Zhang, M. Hu, G. Zhai, C. Wang, Z. Zhang, J. Yang, C. Shen, and D. Tao, “Deepfake generation and detection: A benchmark and survey,” arXiv preprint arXiv:2403.17881, 2024.
D. Kundu and B. L. Evans, “Full-reference visual quality assessment for synthetic images: A subjective study,” in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015, pp. 2374–2378.
A. Bauer, S. Trapp, M. Stenger, R. Leppich, S. Kounev, M. Leznik, K. Chard, and I. Foster, “Comprehensive exploration of synthetic data generation: A survey,” arXiv2401.02524, 2024. [Online]. Available: https://arxiv.org/pdf/2401.02524
J. Liu, X. Huang, T. Huang, L. Chen, Y. Hou, S. Tang, Z. Liu, W. Ouyang, W. Zuo, J. Jiang, and X. Liu, “A comprehensive survey on 3D content generation,” arXiv2402.01166, 2024. [Online]. Available: https://arxiv.org/pdf/2402.01166
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Weinberger, Eds., vol. 27. Curran Associates, Inc., 2014.
A. Ferreira, J. Li, K. L. Pomykala, J. Kleesiek, V. Alves, and J. Egger, “GANbased generation of realistic 3D volumetric data: A systematic review and taxonomy,” Medical Image Analysis, vol. 93, p. 103100, Apr. 2024. [Online]. Available: http://dx.doi.org/10.1016/j.media.2024.103100
A. Radford, L. Metz, and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks,” arXiv:1511.06434, 2015. [Online]. Available: https://arxiv.org/pdf/1511.06434
X. Mao, Q. Li, H. Xie, R. Y. K. Lau, Z. Wang, and S. P. Smolley, “Least squares generative adversarial networks,” arXiv:1611.04076, 2017. [Online]. Available: https://arxiv.org/pdf/1611.04076
M. Mirza and S. Osindero, “Conditional generative adversarial nets,” arXiv:1411.1784, 2014. [Online]. Available: https://arxiv.org/pdf/1411.1784
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein gan,” arXiv:1701.07875, 2017. [Online]. Available: https://arxiv.org/pdf/1701.07875
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of gans for improved quality, stability, and variation,” arXiv:1710.10196, 2018. [Online]. Available: https://arxiv.org/pdf/1710.10196
A. Brock, J. Donahue, and K. Simonyan, “Large scale gan training for high fidelity natural image synthesis,” arXiv:1809.11096, 2019. [Online]. Available: https://arxiv.org/pdf/1809.11096
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” arXiv:1812.04948, 2019. [Online]. Available: https://arxiv.org/pdf/1812.04948
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv:1312.6114, 2022. [Online]. Available: https://arxiv.org/pdf/1312.6114
D. J. Rezende, S. Mohamed, and D. Wierstra, “Stochastic backpropagation and approximate inference in deep generative models,” arXiv:1401.4082, 2014. [Online]. Available: https://arxiv.org/pdf/1401.4082
A. van den Oord, N. Kalchbrenner, and K. Kavukcuoglu, “Pixel recurrent neural networks,” arXiv:1601.06759, 2016. [Online]. Available: https://arxiv.org/pdf/1601.06759
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in Proceedings of the 37th International Conference on Machine Learning, ser. Proceedings of Machine Learning Research, H. D. III and A. Singh, Eds., vol. 119. PMLR, 13–18 Jul 2020, pp. 1597–1607. [Online]. Available: https://proceedings.mlr.press/v119/chen20j.html
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,” arXiv:2003.08934, 2020. [Online]. Available: https://arxiv.org/pdf/2003.08934
C. F. Higham, D. J. Higham, and P. Grindrod, “Diffusion models for generative artificial intelligence: An introduction for applied mathematicians,” arXiv:2312.14977, 2023. [Online]. Available: https://arxiv.org/pdf/2312.14977
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Yang, “Diffusion models: A comprehensive survey of methods and applications,” arXiv:2209.00796, 2024. [Online]. Available: https://arxiv.org/pdf/2209.00796
L. Dinh, D. Krueger, and Y. Bengio, “Nice: Non-linear independent components estimation,” arXiv:1410.8516, 2015. [Online]. Available: https://arxiv.org/pdf/1410.8516
D. J. Rezende and S. Mohamed, “Variational inference with normalizing flows,” arXiv:1505.05770, 2016. [Online]. Available: https://arxiv.org/pdf/1505.05770
L. Dinh, J. Sohl-Dickstein, and S. Bengio, “Density estimation using real nvp,” arXiv:1605.08803, 2017. [Online]. Available: https://arxiv.org/pdf/1605.08803
D. P. Kingma and P. Dhariwal, “Glow: Generative flow with invertible 1x1 convolutions,” arXiv:1807.03039, 2018. [Online]. Available: https://arxiv.org/pdf/1807.03039
G. Papamakarios, T. Pavlakou, and I. Murray, “Masked autoregressive flow for density estimation,” arXiv:1705.07057, 2018. [Online]. Available: https://arxiv.org/pdf/1705.07057
W. Grathwohl, R. T. Q. Chen, J. Bettencourt, I. Sutskever, and D. Duvenaud, “Ffjord: Free-form continuous dynamics for scalable reversible generative models,” arXiv:1810.01367, 2018. [Online]. Available: https://arxiv.org/pdf/1810.01367
L. Peng, X. Qiu, C. Ding, and W. Tie, “Generating 3d point clouds from a single SAR image using 3D reconstruction network,” in 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019, Yokohama, Japan, July 28 - August 2, 2019. IEEE, 2019, pp. 3685–3688. [Online]. Available: https://doi.org/10.1109/IGARSS.2019.8900449
A. Karnewar, O. Wang, T. Ritschel, and N. J. Mitra, “3ingan: Learning a 3D generative model from images of a self-similar scene,” in International Conference on 3D Vision, 3DV 2022, Prague, Czech Republic, September 12-16, 2022. IEEE, 2022, pp. 342–352. [Online]. Available: https://doi.org/10.1109/3DV57658.2022.00046
J. Mangalagiri, D. Chapman, A. Gangopadhyay, Y. Yesha, J. Galita, S. Menon, Y. Yesha, B. Saboury, M. Morris, and P. Nguyen, “Toward generating synthetic CT volumes using a 3D-conditional generative adversarial network,” CoRR, vol. abs/2104.02060, 2021. [Online]. Available: https://arxiv.org/abs/2104.02060
S. Mok and G. Kim, “Simulated intensity rendering of 3D lidar using generative adversarial network,” in IEEE International Conference on Big Data and Smart Computing, BigComp 2021, Jeju Island, South Korea, January 17-20, 2021, H. Unger, J. Kim, U. Kang, C. So-In, J. Du, W. Saad, Y. Ha, C. Wagner, J. Bourgeois, C. Sathitwiriyawong, H. Kwon, and C. K. Leung, Eds. IEEE, 2021, pp. 295–297. [Online]. Available: https://doi.org/10.1109/BigComp51126.2021.00062
C. K. Chong and E. T. W. Ho, “Synthesis of 3D MRI brain images with shape and texture generative adversarial deep neural networks,” IEEE Access, vol. 9, pp. 64 747–64 760, 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3075608
P. M. Chu, Y. Sung, and K. Cho, “Generative adversarial network-based method for transforming single RGB image into 3D point cloud,” IEEE Access, vol. 7, pp. 1021–1029, 2019. [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2886213
A. Dundar, J. Gao, A. Tao, and B. Catanzaro, “Progressive learning of 3D reconstruction network from 2d GAN data,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 46, no. 2, pp. 793–804, 2024. [Online]. Available: https://doi.org/10.1109/TPAMI.2023.3324806
——, “Fine detailed texture learning for 3D meshes with generative models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 45, no. 12, pp. 14 563–14 574, 2023. [Online]. Available: https://doi.org/10.1109/TPAMI.2023.3319429
X. Zheng, Y. Liu, P. Wang, and X. Tong, “Sdf-stylegan: Implicit sdf-based stylegan for 3D shape generation,” Comput. Graph. Forum, vol. 41, no. 5, pp. 52–63, 2022. [Online]. Available: https://doi.org/10.1111/cgf.14602
H. Xu, F. Omitaomu, S. Sabri, X. Li, and Y. Song, “Leveraging generative ai for smart city digital twins: A survey on the autonomous generation of data, scenarios, 3D city models, and urban designs,” arXiv:2405.19464, 2024. [Online]. Available: https://arxiv.org/pdf/2405.19464
K. Zhao and A. Larsen, “Challenges and opportunities in 3D content generation,” arXiv:2405.15335, 2024. [Online]. Available: https://arxiv.org/pdf/2405.15335
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” arXiv:2209.14988, 2022. [Online]. Available: https://arxiv.org/pdf/2209.14988
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:2211.10440, 2023. [Online]. Available: https://arxiv.org/pdf/2211.10440
F. Hong, M. Zhang, L. Pan, Z. Cai, L. Yang, and Z. Liu, “Avatarclip: Zero-shot text-driven generation and animation of 3d avatars,” arXiv:2205.08535, 2022. [Online]. Available: https://arxiv.org/pdf/2205.08535
Z. Chen, G. Wang, and Z. Liu, “Scenedreamer: Unbounded 3d scene generation from 2d image collections,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 12, p. 15562–15576, Dec. 2023. [Online]. Available: http://dx.doi.org/10.1109/TPAMI.2023.3321857
E. Richardson, G. Metzer, Y. Alaluf, R. Giryes, and D. Cohen-Or, “Texture: Textguided texturing of 3d shapes,” arXiv:2302.01721, 2023. [Online]. Available: https://arxiv.org/pdf/2302.01721
R. Chen, Y. Chen, N. Jiao, and K. Jia, “Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation,” arXiv:2303.13873, 2023. [Online]. Available: https://arxiv.org/pdf/2303.13873
L. Hollein, A. Cao, A. Owens, J. Johnson, and M. Nießner, “Text2room: Extracting textured 3d meshes from 2d text-to-image models,” arXiv:2303.11989, 2023. [Online]. Available: https://www.researchgate.net/publication/377428396_Text2Room_Extracting_ Textured_3D_Meshes_from_2D_Text-to-Image_Models
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:2305.16213, 2023. [Online]. Available: https://arxiv.org/pdf/2305.16213
X. Han, Y. Cao, K. Han, X. Zhu, J. Deng, Y.-Z. Song, T. Xiang, and K.-Y. K. Wong, “Headsculpt: Crafting 3d head avatars with text,” arXiv:2306.03038, 2023. [Online]. Available: https://arxiv.org/pdf/2306.03038
N. Kolotouros, T. Alldieck, A. Zanfir, E. G. Bazavan, M. Fieraru, and C. Sminchisescu, “Dreamhuman: Animatable 3d avatars from text,” arXiv:2306.09329, 2023. [Online]. Available: https://arxiv.org/pdf/2306.09329
J. Tang, J. Ren, H. Zhou, Z. Liu, and G. Zeng, “Dreamgaussian: Generative gaussian splatting for efficient 3d content creation,” arXiv:2309.16653, 2024. [Online]. Available: https://arxiv.org/pdf/2309.16653
T. Cao, K. Kreis, S. Fidler, N. Sharp, and K. Yin, “Texfusion: Synthesizing 3d textures with text-guided image diffusion models,” arXiv:2310.13772, 2023. [Online]. Available: https://arxiv.org/pdf/2310.13772
J. Chung, S. Lee, H. Nam, J. Lee, and K. M. Lee, “Luciddreamer: Domain-free generation of 3d gaussian splatting scenes,” arXiv:2311.13384, 2023. [Online]. Available: https://arxiv.org/pdf/2311.13384
D. Z. Chen, H. Li, H.-Y. Lee, S. Tulyakov, and M. Nießner, “Scenetex: High-quality texture synthesis for indoor scenes via diffusion priors,” arXiv:2311.17261, 2023. [Online]. Available: https://arxiv.org/pdf/2311.17261
T. Huang, Y. Zeng, Z. Zhang, W. Xu, H. Xu, S. Xu, R. W. H. Lau, and W. Zuo, “Dreamcontrol: Control-based text-to-3d generation with 3d self-prior,” arXiv:2312.06439, 2024. [Online]. Available: https://arxiv.org/pdf/2312.06439
C. H. Lin, H.-Y. Lee, W. Menapace, M. Chai, A. Siarohin, M.-H. Yang, and S. Tulyakov, “Infinicity: Infinite-scale city synthesis,” arXiv:2301.09637, 2023. [Online]. Available: https://arxiv.org/pdf/2301.09637
J. Deng, W. Chai, J. Huang, Z. Zhao, Q. Huang, M. Gao, J. Guo, S. Hao, W. Hu, J.-N. Hwang, X. Li, and G. Wang, “Citycraft: A real crafter for 3D city generation,” arXiv:2406.04983, 2024. [Online]. Available: https://arxiv.org/pdf/2406.04983
M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “Smpl: A skinned multiperson linear model,” in ACM Transactions on Graphics, vol. 34, no. 6. IEEE, 2015, p. 248:1–248:16.
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,” arXiv:1803.08495, 2018. [Online]. Available: https://arxiv.org/pdf/1803.08495
K. Schwarz, Y. Liao, M. Niemeyer, and A. Geiger, “Graf: Generative radiance fields for 3d-aware image synthesis,” in Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 20 154–20 166. [Online]. Available: https://proceedings.neurips.cc/ paper_files/paper/2020/file/e92e1b476bb5262d793fd40931e0ed53-Paper.pdf
E. Corona, A. Pumarola, G. Alenyà, G. Pons-Moll, and F. Moreno-Noguer, “Smplicit: Topologyaware generative model for clothed people,” in CVPR, 2021.
Y. Hong, B. Peng, H. Xiao, L. Liu, and J. Zhang, “Headnerf: A real-time nerf-based parametric head model,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022.
X. Chen, T. Jiang, J. Song, J. Yang, M. J. Black, A. Geiger, and O. Hilliges, “gdna: Towards generative detailed neural avatars,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 20 395–20 405.
R. Fu, X. Zhan, Y. Chen, D. Ritchie, and S. Sridhar, “Shapecrafter: A recursive text-conditioned 3d shape generation model,” CoRR, vol. abs/2207.09446, 2022. [Online]. Available: https://doi.org/10.48550/arXiv.2207.09446
M. A. Bautista, P. Guo, S. Abnar, W. Talbott, A. Toshev, Z. Chen, L. Dinh, S. Zhai, H. Goh, D. Ulbricht, A. Dehghan, and J. Susskind, “Gaudi: A neural architect for immersive 3d scene generation,” arXiv:2207.13751, 2022. [Online]. Available: https://arxiv.org/abs/2207.137511
A. Nichol, H. Jun, P. Dhariwal, P. Mishkin, and M. Chen, “Point-e: A system for generating 3d point clouds from complex prompts,” arXiv:2212.08751, 2022. [Online]. Available: https://arxiv.org/pdf/2212.08751
S. W. Kim, B. Brown, K. Yin, K. Kreis, K. Schwarz, D. Li, R. Rombach, A. Torralba, and S. Fidler, “Neuralfield-ldm: Scene generation with hierarchical latent diffusion models,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17-24, 2023. IEEE, 2023, pp. 8496–8506. [Online]. Available: https://doi.org/10.1109/CVPR52729.2023.00821
H. Jun and A. Nichol, “Shap-e: Generating conditional 3d implicit functions,” arXiv:2305.02463, 2023. [Online]. Available: https://arxiv.org/pdf/2305.02463
T. Huang, Y. Zeng, B. Dong, H. Xu, S. Xu, R. W. H. Lau, and W. Zuo, “Textfield3d: Towards enhancing open-vocabulary 3d generation with noisy text fields,” arXiv:2309.17175, 2024. [Online]. Available: https://arxiv.org/abs/2309.17175
Y. Hong, K. Zhang, J. Gu, S. Bi, Y. Zhou, D. Liu, F. Liu, K. Sunkavalli, T. Bui, and H. Tan, “LRM: Large reconstruction model for single image to 3d,” in The Twelfth International Conference on Learning Representations, 2024. [Online]. Available: https://openreview.net/forum?id=sllU8vvsFF
Y. Xu, H. Tan, F. Luan, S. Bi, P. Wang, J. Li, Z. Shi, K. Sunkavalli, G. Wetzstein, Z. Xu, and K. Zhang, “Dmv3d: Denoising multi-view diffusion using 3d large reconstruction model,” arXiv:2311.09217, 2023. [Online]. Available: https://arxiv.org/abs/2311.09217
X. Ren, J. Huang, X. Zeng, K. Museth, S. Fidler, and F. Williams, “Xcube (X3): Large-scale 3d generative modeling using sparse voxel hierarchies,” arXiv:2312.03806, 2023. [Online]. Available: https://arxiv.org/abs/2312.03806
A. Chen, R. Liu, L. Xie, Z. Chen, H. Su, and J. Yu, “Sofgan: A portrait image generator with dynamic styling,” ACM Trans. Graph., vol. 41, no. 1, feb 2022. [Online]. Available: https://doi.org/10.1145/3470848
U. Singer, S. Sheynin, A. Polyak, O. Ashual, I. Makarov, F. Kokkinos, N. Goyal, A. Vedaldi, D. Parikh, J. Johnson, and Y. Taigman, “Text-to-4d dynamic scene generation,” arXiv:2301.11280, 2023. [Online]. Available: https://arxiv.org/abs/2301.11280
Y. Chen, J. Fang, Y. Huang, T. Yi, X. Zhang, L. Xie, X. Wang, W. Dai, H. Xiong, and Q. Tian, “Cascade-zero123: One image to highly consistent 3d with self-prompted nearby views,” arXiv:2312.04424, 2023. [Online]. Available: https://arxiv.org/abs/2312.04424
M. Liu, C. Xu, H. Jin, L. Chen, M. V. T, Z. Xu, and H. Su, “One-2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization,” arXiv:2306.16928, 2023. [Online]. Available: https://arxiv.org/abs/2306.16928
S. Tang, F. Zhang, J. Chen, P. Wang, and Y. Furukawa, “Mvdiffusion: Enabling holistic multi-view image generation with correspondence-aware diffusion,” arXiv:2307.01097, 2023. [Online]. Available: https://arxiv.org/abs/2307.01097
X. Huang, R. Shao, Q. Zhang, H. Zhang, Y. Feng, Y. Liu, and Q. Wang, “Humannorm: Learning normal diffusion model for high-quality and realistic 3d human generation,” arXiv:2310.01406, 2023. [Online]. Available: https://arxiv.org/abs/2310.01406
X. Long, Y.-C. Guo, C. Lin, Y. Liu, Z. Dou, L. Liu, Y. Ma, S.-H. Zhang, M. Habermann, C. Theobalt, and W. Wang, “Wonder3d: Single image to 3d using cross-domain diffusion,” arXiv:2310.15008, 2023. [Online]. Available: https://arxiv.org/abs/2310.15008
J. Sun, B. Zhang, R. Shao, L. Wang, W. Liu, Z. Xie, and Y. Liu, “Dreamcraft3d: Hierarchical 3d generation with bootstrapped diffusion prior,” arXiv:2310.16818, 2023. [Online]. Available: https://arxiv.org/abs/2310.16818
Y. Jiang, L. Zhang, J. Gao, W. Hu, and Y. Yao, “Consistent4d: Consistent 360 dynamic object generation from monocular video,” arXiv:2311.02848, 2023. [Online]. Available: https://arxiv.org/abs/2311.02848
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:2311.06214, 2023. [Online]. Available: https://arxiv.org/abs/2311.06214
J. Schult, S. Tsai, L. Höllein, B. Wu, J. Wang, C.-Y. Ma, K. Li, X. Wang, F. Wimbauer, Z. He, P. Zhang, B. Leibe, P. Vajda, and J. Hou, “Controlroom3d: Room generation using semantic proxy rooms,” arXiv:2312.05208, 2023. [Online]. Available: https://arxiv.org/abs/2312.05208
Q. Zhang, C. Wang, A. Siarohin, P. Zhuang, Y. Xu, C. Yang, D. Lin, B. Zhou, S. Tulyakov, and H.-Y. Lee, “Scenewiz3d: Towards text-guided 3d scene composition,” arXiv:2312.08885, 2023. [Online]. Available: https://arxiv.org/abs/2312.08885
Y. Yin, D. Xu, Z. Wang, Y. Zhao, and Y. Wei, “4dgen: Grounded 4d content generation with spatial-temporal consistency,” arXiv:2312.17225, 2024. [Online]. Available: https://arxiv.org/abs/2312.17225
M. Liu, R. Shi, L. Chen, Z. Zhang, C. Xu, X. Wei, H. Chen, C. Zeng, J. Gu, and H. Su, “One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion,” arXiv:2311.07885, 2023. [Online]. Available: https://arxiv.org/abs/2311.07885
J. Ren, L. Pan, J. Tang, C. Zhang, A. Cao, G. Zeng, and Z. Liu, “Dreamgaussian4d: Generative 4d gaussian splatting,” arXiv:2312.17142, 2024. [Online]. Available: https://arxiv.org/abs/2312.17142
Y. Zhao, Z. Yan, E. Xie, L. Hong, Z. Li, and G. H. Lee, “Animate124: Animating one image to 4d dynamic scene,” arXiv:2311.14603, 2024. [Online]. Available: https://arxiv.org/abs/2311.14603
Z. Chen, F. Wang, Y. Wang, and H. Liu, “Text-to-3d using gaussian splatting,” arXiv:2309.16585, 2024. [Online]. Available: https://arxiv.org/abs/2309.16585
Y. Liu, C. Lin, Z. Zeng, X. Long, L. Liu, T. Komura, and W. Wang, “Syncdreamer: Generating multiview-consistent images from a single-view image,” arXiv preprint arXiv:2309.03453, 2023.
Y. Shi, P. Wang, J. Ye, L. Mai, K. Li, and X. Yang, “MVDream: Multi-view diffusion for 3d generation,” in The Twelfth International Conference on Learning Representations, 2024. [Online]. Available: https://openreview.net/forum?id=FUgrjq2pbB
Y. Shen, W.-C. Ma, and S. Wang, “Sgam: building a virtual 3d world through simultaneous generation and mapping,” in Proceedings of the 36th International Conference on Neural Information Processing Systems, ser. NIPS ’22. Red Hook, NY, USA: Curran Associates Inc., 2024.
H. Xie, Z. Chen, F. Hong, and Z. Liu, “Citydreamer: Compositional generative model of unbounded 3D cities,” arXiv:2309.00610, 2024. [Online]. Available: https: //arxiv.org/pdf/2309.00610
Y. Ming, X. Yang, W. Wang, Z. Chen, J. Feng, Y. Xing, and G. Zhang, “Benchmarking neural radiance fields for autonomous robots: An overview,” arXiv:2405.05526, 2024. [Online]. Available: https://arxiv.org/pdf/2405.05526
W. K. Chan, P. Wang, and R. C.-H. Yeow, “Creation of novel soft robot designs using generative ai,” arXiv:2405.01824, 2024. [Online]. Available: https://arxiv.org/pdf/2405.01824
S. Nasiriany, A. Maddukuri, L. Zhang, A. Parikh, A. Lo, A. Joshi, A. Mandlekar, and Y. Zhu, “Robocasa: Large-scale simulation of everyday tasks for generalist robots,” arXiv:2406.02523, 2024. [Online]. Available: https://arxiv.org/pdf/2406.02523
J. A. Barragan, J. Zhang, H. Zhou, A. Munawar, and P. Kazanzides, “Realistic data generation for 6d pose estimation of surgical instruments,” arXiv:2406.07328, 2024. [Online]. Available: https://arxiv.org/pdf/2406.07328
M. A. J. au2, “Y-gan: A generative adversarial network for depthmap estimation from multi-camera stereo images,” arXiv:1906.00932, 2019. [Online]. Available: https://arxiv.org/pdf/1906.00932
Z. Song, C. Wang, J. Sheng, C. Zhang, G. Yu, J. Fan, and T. Chen, “Moviellm: Enhancing long video understanding with ai-generated movies,” arXiv:2403.01422, 2024. [Online]. Available: https://arxiv.org/pdf/2403.01422
G. Chen, Y.-D. Zheng, J. Wang, J. Xu, Y. Huang, J. Pan, Y. Wang, Y. Wang, Y. Qiao, T. Lu, and L. Wang, “Videollm: Modeling video sequence with large language models,” arXiv:2305.13292, 2023. [Online]. Available: https://arxiv.org/pdf/2305.13292
S. Huang, H. Zhang, Y. Gao, Y. Hu, and Z. Qin, “From image to video, what do we need in multimodal llms?” arXiv:2404.11865, 2024. [Online]. Available: https://arxiv.org/pdf/2404.11865
T. Brooks, J. Hellsten, M. Aittala, T.-C. Wang, T. Aila, J. Lehtinen, M.-Y. Liu, A. Efros, and T. Karras, “Generating long videos of dynamic scenes,” in Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35. Curran Associates, Inc., 2022, pp. 31 769–31 781. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/file/ ce208d95d020b023cba9e64031db2584-Paper-Conference.pdf
D. Antić, G. Tiwari, B. Ozcomlekci, R. Marin, and G. Pons-Moll, “Close: A 3D clothing segmentation dataset and model,” arXiv:2401.12051, 2024. [Online]. Available: https://arxiv.org/pdf/2401.12051
V. Sklyarova, J. Chelishev, A. Dogaru, I. Medvedev, V. Lempitsky, and E. Zakharov, “Neural haircut: Prior-guided strand-based hair reconstruction,” arXiv:2306.05872, 2023. [Online]. Available: https://arxiv.org/pdf/2306.05872
Q.-Y. Zhou, J. Park, and V. Koltun, “Open3D: A modern library for 3D data processing,” arXiv:1801.09847, 2018. [Online]. Available: https://arxiv.org/pdf/1801.09847
B. Kovalerchuk, K. Nazemi, R. Andonie, N. Datia, and E. Banissi, Integrating Artificial Intelligence and Visualization for Visual Knowledge Discovery. Springer, 2022.
B. Kovalerchuk, K. Nazemi, R. Andonie, N. Datia, and E. Bannissi, Artificial Intelligence and Visualization: Advancing Visual Knowledge Discovery. Springer, 2024.
S. Bai and J. Li, “Progress and prospects in 3D generative ai: A technical overview including 3D human,” arXiv:2401.02620, 2024. [Online]. Available: https://arxiv.org/pdf/2401.02620
B. Kovalerchuk, Visual Knowledge Discovery and Machine Learning. Springer, 2018, vol. 144.
J. Martinez and B. Kovalerchuk, “General line coordinates in 3D,” in 2023 27th International Conference Information Visualisation (IV), 2023, pp. 308–315.
R. Schwartz, J. Dodge, N. A. Smith, and O. Etzioni, “Green ai,” arXiv::1907.10597, 2019. [Online]. Available: https://arxiv.org/abs/1907.10597
Y. Zhou, X. Lin, X. Zhang, M. Wang, G. Jiang, H. Lu, Y. Wu, K. Zhang, Z. Yang, K. Wang, Y. Sui, F. Jia, Z. Tang, Y. Zhao, H. Zhang, T. Yang, W. Chen, Y. Mao, Y. Li, D. Bao, Y. Li, H. Liao, T. Liu, J. Liu, J. Guo, X. Zhao, Y. WEI, H. Qian, Q. Liu, X. Wang, W. Kin, Chan, C. Li, Y. Li, S. Yang, J. Yan, C. Mou, S. Han, W. Jin, G. Zhang, and X. Zeng, “On the opportunities of green computing: A survey,” arXiv:2311.00447, 2023. [Online]. Available: https://arxiv.org/abs/2311.00447
C. Clemm, L. Stobbe, K. Wimalawarne, and J. Druschke, “Towards green ai: Current status and future research,” arXiv::2103.10237, 2024. [Online]. Available: https://arxiv.org/abs/2407.10237
A. Asperti, D. Evangelista, and E. L. Piccolomini, “A survey on variational autoencoders from a greenai perspective,” arXiv::2103.01071, 2021. [Online]. Available: https://arxiv.org/abs/ 2103.01071
Additional Files
Published
Issue
Section
License
Copyright (c) 2025 Stelian Matase, Razvan Andonie
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
ONLINE OPEN ACCES: Acces to full text of each article and each issue are allowed for free in respect of Attribution-NonCommercial 4.0 International (CC BY-NC 4.0.
You are free to:
-Share: copy and redistribute the material in any medium or format;
-Adapt: remix, transform, and build upon the material.
The licensor cannot revoke these freedoms as long as you follow the license terms.
DISCLAIMER: The author(s) of each article appearing in International Journal of Computers Communications & Control is/are solely responsible for the content thereof; the publication of an article shall not constitute or be deemed to constitute any representation by the Editors or Agora University Press that the data presented therein are original, correct or sufficient to support the conclusions reached or that the experiment design or methodology is adequate.
