Built on

Yuen, S.C.Y., Yaoyuneyong, G., Johnson, E.: Augmented reality: An overview and five directions for ar in education. Journal of Educational Technology Development and Exchange (JETDE) 4
2011
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Original
2014
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)
2018
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G., Cohen-Or, D.: An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 (2022)
Original
2022
Earlier work this paper cites.
Gal, R., Patashnik, O., Maron, H., Bermano, A.H., Chechik, G., Cohen-Or, D.: Stylegan-nada: Clip-guided domain adaptation of image generators. ACM Transactions on Graphics (TOG) 41
2022
Earlier work this paper cites.
Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: Dreamfusion: Text-to-3d using 2d diffusion (2022)
2022
Earlier work this paper cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
2022
Earlier work this paper cites.
Yi, X., Tang, K., Hua, X.S., Lim, J.H., Zhang, H.: Identifying hard noise in long-tailed sample distribution. In: European Conference on Computer Vision. pp. 739–756. Springer (2022)
2022
Earlier work this paper cites.
Bai, J., Dong, Z., Feng, A., Zhang, X., Ye, T., Zhou, K., Shou, M.Z.: Integrating view conditions for image synthesis (2023)
2023
Earlier work this paper cites.
Bao, C., Zhang, Y., Yang, B., Fan, T., Yang, Z., Bao, H., Zhang, G., Cui, Z.: Sine: Semantic-driven image-based nerf editing with prior-guided editing field. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20919–20929 (2023)
2023
Earlier work this paper cites.
Cao, Y., Cao, Y.P., Han, K., Shan, Y., Wong, K.Y.K.: Dreamavatar: Text-and-shape guided 3d human avatar generation via diffusion models. arXiv preprint arXiv:2304.00916 (2023)
Original
2023
Earlier work this paper cites.
Chen, M., Xie, J., Laina, I., Vedaldi, A.: Shap-editor: Instruction-guided latent 3d editing in seconds. arXiv preprint arXiv:2312.09246 (2023)
Original
2023
Earlier work this paper cites.
Chen, Y., Chen, A., Chen, S., Yi, R.: Plasticine3d: Non-rigid 3d editting with text guidance. arXiv preprint arXiv:2312.10111 (2023)
Original
2023
Earlier work this paper cites.
Chen, Y., Chen, Z., Zhang, C., Wang, F., Yang, X., Wang, Y., Cai, Z., Yang, L., Liu, H., Lin, G.: Gaussianeditor: Swift and controllable 3d editing with gaussian splatting (2023)
2023
Earlier work this paper cites.

Similar

Cheng, X., Yang, T., Wang, J., Li, Y., Zhang, L., Zhang, J., Yuan, L.: Progressive3d: Progressively local editing for text-to-3d content creation with complex semantic prompts. arXiv preprint arXiv:2310.11784 (2023)
Original
2023
Cited alongside, same era.
Fang, J., Wang, J., Zhang, X., Xie, L., Tian, Q.: Gaussianeditor: Editing 3d gaussians delicately with text instructions (2023)
2023
Cited alongside, same era.
Gan, Y., Park, S., Schubert, A., Philippakis, A., Alaa, A.M.: Instructcv: Instruction-tuned text-to-image diffusion models as vision generalists (2023)
2023
Cited alongside, same era.
Haque, A., Tancik, M., Efros, A.A., Holynski, A., Kanazawa, A.: Instruct-nerf2nerf: Editing 3d scenes with instructions (2023)
2023
Cited alongside, same era.
Hertz, A., Aberman, K., Cohen-Or, D.: Delta denoising score (2023)
2023
Cited alongside, same era.

Then

Tang, J., Ren, J., Zhou, H., Liu, Z., Zeng, G.: Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309.16653 (2023)
Original
2023
Later among the works it cites.
Wang, C., He, M., Chai, M., Chen, D., Liao, J.: Mesh-guided neural implicit field editing (2023)
2023
Later among the works it cites.
Wu, Z., Zhou, P., Kawaguchi, K., Zhang, H.: Fast diffusion model (2023)
2023
Later among the works it cites.
Xu, S., Huang, Y., Pan, J., Ma, Z., Chai, J.: Inversion-free image editing with natural language (2023)
2023
Later among the works it cites.
Yi, X., Deng, J., Sun, Q., Hua, X.S., Lim, J.H., Zhang, H.: Invariant training 2d-3d joint hard samples for few-shot point cloud recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14463–14474 (2023)
2023

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2024

View-Consistent 3D Editing with Gaussian Splatting

Wang, Yuxuan, Yi, Xuanyu, Wu, Zike et al.

Understand

The advent of 3D Gaussian Splatting (3DGS) has revolutionized 3D editing, offering efficient, high-fidelity rendering and enabling precise local manipulations.

Currently, diffusion-based 2D editing models are harnessed to modify multi-view rendered images, which then guide the editing of 3DGS models.
However, this approach faces a critical issue of multi-view inconsistency, where the guidance images exhibit significant discrepancies across views, leading to mode collapse and visual artifacts of 3DGS.
To this end, we introduce View-consistent Editing (VcEdit), a novel framework that seamlessly incorporates 3DGS into image editing processes, ensuring multi-view consistency in edited guidance images and effectively mitigating mode collapse issues.

Built on

Yuen, S.C.Y., Yaoyuneyong, G., Johnson, E.: Augmented reality: An overview and five directions for ar in education. Journal of Educational Technology Development and Exchange (JETDE) 4
2011
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Original
2014
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Original
2014
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 586–595 (2018)
2018
Earlier work this paper cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International conference on machine learning. pp. 8748–8763. PMLR (2021)
2021
Earlier work this paper cites.
Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A.H., Chechik, G., Cohen-Or, D.: An image is worth one word: Personalizing text-to-image generation using textual inversion. arXiv preprint arXiv:2208.01618 (2022)
Original
2022
Earlier work this paper cites.
Gal, R., Patashnik, O., Maron, H., Bermano, A.H., Chechik, G., Cohen-Or, D.: Stylegan-nada: Clip-guided domain adaptation of image generators. ACM Transactions on Graphics (TOG) 41
2022
Earlier work this paper cites.
Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: Dreamfusion: Text-to-3d using 2d diffusion (2022)
2022
Earlier work this paper cites.
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10684–10695 (2022)
2022
Earlier work this paper cites.
Yi, X., Tang, K., Hua, X.S., Lim, J.H., Zhang, H.: Identifying hard noise in long-tailed sample distribution. In: European Conference on Computer Vision. pp. 739–756. Springer (2022)
2022
Earlier work this paper cites.
Bai, J., Dong, Z., Feng, A., Zhang, X., Ye, T., Zhou, K., Shou, M.Z.: Integrating view conditions for image synthesis (2023)
2023
Earlier work this paper cites.
Bao, C., Zhang, Y., Yang, B., Fan, T., Yang, Z., Bao, H., Zhang, G., Cui, Z.: Sine: Semantic-driven image-based nerf editing with prior-guided editing field. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20919–20929 (2023)
2023
Earlier work this paper cites.
Cao, Y., Cao, Y.P., Han, K., Shan, Y., Wong, K.Y.K.: Dreamavatar: Text-and-shape guided 3d human avatar generation via diffusion models. arXiv preprint arXiv:2304.00916 (2023)
Original
2023
Earlier work this paper cites.
Chen, M., Xie, J., Laina, I., Vedaldi, A.: Shap-editor: Instruction-guided latent 3d editing in seconds. arXiv preprint arXiv:2312.09246 (2023)
Original
2023
Earlier work this paper cites.
Chen, Y., Chen, A., Chen, S., Yi, R.: Plasticine3d: Non-rigid 3d editting with text guidance. arXiv preprint arXiv:2312.10111 (2023)
Original
2023
Earlier work this paper cites.
Chen, Y., Chen, Z., Zhang, C., Wang, F., Yang, X., Wang, Y., Cai, Z., Yang, L., Liu, H., Lin, G.: Gaussianeditor: Swift and controllable 3d editing with gaussian splatting (2023)
2023
Earlier work this paper cites.

Similar

Cheng, X., Yang, T., Wang, J., Li, Y., Zhang, L., Zhang, J., Yuan, L.: Progressive3d: Progressively local editing for text-to-3d content creation with complex semantic prompts. arXiv preprint arXiv:2310.11784 (2023)
Original
2023
Cited alongside, same era.
Fang, J., Wang, J., Zhang, X., Xie, L., Tian, Q.: Gaussianeditor: Editing 3d gaussians delicately with text instructions (2023)
2023
Cited alongside, same era.
Gan, Y., Park, S., Schubert, A., Philippakis, A., Alaa, A.M.: Instructcv: Instruction-tuned text-to-image diffusion models as vision generalists (2023)
2023
Cited alongside, same era.
Haque, A., Tancik, M., Efros, A.A., Holynski, A., Kanazawa, A.: Instruct-nerf2nerf: Editing 3d scenes with instructions (2023)
2023
Cited alongside, same era.
Hertz, A., Aberman, K., Cohen-Or, D.: Delta denoising score (2023)
2023
Cited alongside, same era.

Then

Tang, J., Ren, J., Zhou, H., Liu, Z., Zeng, G.: Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309.16653 (2023)
Original
2023
Later among the works it cites.
Wang, C., He, M., Chai, M., Chen, D., Liao, J.: Mesh-guided neural implicit field editing (2023)
2023
Later among the works it cites.
Wu, Z., Zhou, P., Kawaguchi, K., Zhang, H.: Fast diffusion model (2023)
2023
Later among the works it cites.
Xu, S., Huang, Y., Pan, J., Ma, Z., Chai, J.: Inversion-free image editing with natural language (2023)
2023
Later among the works it cites.
Yi, X., Deng, J., Sun, Q., Hua, X.S., Lim, J.H., Zhang, H.: Invariant training 2d-3d joint hard samples for few-shot point cloud recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14463–14474 (2023)
2023

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Kamata, H., Sakuma, Y., Hayakawa, A., Ishii, M., Narihira, T.: Instruct 3d-to-3d: Text instruction guided 3d-to-3d conversion. arXiv preprint arXiv:2303.15780 (2023)

Original

2023

Cited alongside, same era.

Karim, N., Khalid, U., Iqbal, H., Hua, J., Chen, C.: Free-editor: Zero-shot text-driven 3d scene editing. arXiv preprint arXiv:2312.13663 (2023)

Original

2023

Cited alongside, same era.

Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42

2023

Cited alongside, same era.

Khalid, U., Iqbal, H., Karim, N., Hua, J., Chen, C.: Latenteditor: Text driven local editing of 3d scenes. arXiv preprint arXiv:2312.09313 (2023)

Original

2023

Cited alongside, same era.

Koo, J., Park, C., Sung, M.: Posterior distillation sampling (2023)

2023

Cited alongside, same era.

Li, Y., Dou, Y., Shi, Y., Lei, Y., Chen, X., Zhang, Y., Zhou, P., Ni, B.: Focaldreamer: Text-driven 3d editing via focal-fusion assembly (2023)

2023

Cited alongside, same era.

Mikaeili, A., Perel, O., Safaee, M., Cohen-Or, D., Mahdavi-Amiri, A.: Sked: Sketch-guided text-based 3d editing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14607–14619 (2023)

2023

Cited alongside, same era.