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We introduce DragAPart, a method that, given an image and a set of drags as input, generates a new image of the same object that responds to the action of the drags.
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Xiang, F., Qin, Y., Mo, K., Xia, Y., Zhu, H., Liu, F., Liu, M., Jiang, H., Yuan, Y., Wang, H., Yi, L., Chang, A.X., Guibas, L.J., Su, H.: SAPIEN: A simulated part-based interactive environment. In: CVPR (2020)
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Baranchuk, D., Voynov, A., Rubachev, I., Khrulkov, V., Babenko, A.: Label-efficient semantic segmentation with diffusion models. In: ICLR (2021)
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Blattmann, A., Milbich, T., Dorkenwald, M., Ommer, B.: iPOKE: Poking a still image for controlled stochastic video synthesis. In: ICCV (2021)
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Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: ICCV (2021)
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Gadre, S.Y., Ehsani, K., Song, S.: Act the part: Learning interaction strategies for articulated object part discovery. In: ICCV (2021)
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Mo, K., Guibas, L.J., Mukadam, M., Gupta, A.K., Tulsiani, S.: Where2act: From pixels to actions for articulated 3d objects. In: ICCV (2021)
2021
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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: ICML (2021)
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Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: ICML (2021)
2021
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Reizenstein, J., Shapovalov, R., Henzler, P., Sbordone, L., Labatut, P., Novotny, D.: Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction. In: ICCV (2021)
2021
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Song, Y., Sohl-Dickstein, J., Kingma, D.P., Kumar, A., Ermon, S., Poole, B.: Score-based generative modeling through stochastic differential equations. In: ICLR (2021)
2021
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Collins, J., Goel, S., Deng, K., Luthra, A., Xu, L., Gundogdu, E., Zhang, X., Yago Vicente, T.F., Dideriksen, T., Arora, H., Guillaumin, M., Malik, J.: Abo: Dataset and benchmarks for real-world 3d object understanding. In: CVPR (2022)
2022
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2022
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Jiang, Z., Hsu, C.C., Zhu, Y.: Ditto: Building digital twins of articulated objects from interaction. In: CVPR (2022)
2022
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Liu, L., Xu, W., Fu, H., Qian, S., Yu, Q., Han, Y., Lu, C.: AKB-48: A real-world articulated object knowledge base. In: CVPR (2022)
2022
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von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., Wolf, T.: Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers (2022)
2022
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Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: CVPR (2022)
2022
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Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E.L., Ghasemipour, K., Gontijo Lopes, R., Karagol Ayan, B., Salimans, T., et al.: Photorealistic text-to-image diffusion models with deep language understanding. In: NeurIPS (2022)
2022
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Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., et al.: Laion-5b: An open large-scale dataset for training next generation image-text models. In: NeurIPS (2022)
2022
Cited alongside, same era.
Pan, X., Tewari, A., Leimkühler, T., Liu, L., Meka, A., Theobalt, C.: Drag your gan: Interactive point-based manipulation on the generative image manifold. In: ACM SIGGRAPH (2023)
2023
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Peebles, W., Xie, S.: Scalable diffusion models with transformers. In: ICCV (2023)
2023
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Poole, B., Jain, A., Barron, J.T., Mildenhall, B.: DreamFusion: Text-to-3d using 2d diffusion. In: ICLR (2023)
2023
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Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: CVPR (2023)
2023
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Singer, U., Polyak, A., Hayes, T., Yin, X., An, J., Zhang, S., Hu, Q., Yang, H., Ashual, O., Gafni, O., Parikh, D., Gupta, S., Taigman, Y.: Make-a-video: Text-to-video generation without text-video data. In: ICLR (2022)
2022
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Tevet, G., Raab, S., Gordon, B., Shafir, Y., Cohen-or, D., Bermano, A.H.: Human motion diffusion model. In: ICLR (2022)
2022
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Betker, J., Goh, G., Jing, L., Brooks, T., Wang, J., Li, L., Ouyang, L., Zhuang, J., Lee, J., Guo, Y., et al.: Improving image generation with better captions. Tech. rep., OpenAI (2023), https://cdn.openai.com/papers/dall-e-3.pdf
2023
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2023
Cited alongside, same era.
Brooks, T., Holynski, A., Efros, A.A.: Instructpix2pix: Learning to follow image editing instructions. In: CVPR (2023)
2023
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Cao, M., Wang, X., Qi, Z., Shan, Y., Qie, X., Zheng, Y.: Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing. In: ICCV (2023)
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Deitke, M., Liu, R., Wallingford, M., Ngo, H., Michel, O., Kusupati, A., Fan, A., Laforte, C., Voleti, V., Gadre, S.Y., VanderBilt, E., Kembhavi, A., Vondrick, C., Gkioxari, G., Ehsani, K., Schmidt, L., Farhadi, A.: Objaverse-XL: A universe of 10M+ 3D objects. In: NeurIPS (2023)
2023
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2023
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2023
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2023
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Watson, D., Chan, W., Brualla, R.M., Ho, J., Tagliasacchi, A., Norouzi, M.: Novel view synthesis with diffusion models. In: ICLR (2023)
2023
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2023
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Wu, S., Jakab, T., Rupprecht, C., Vedaldi, A.: DOVE: Learning deformable 3d objects by watching videos. IJCV (2023)
2023
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Wu, S., Li, R., Jakab, T., Rupprecht, C., Vedaldi, A.: Magicpony: Learning articulated 3d animals in the wild. In: CVPR (2023)
2023
Later among the works it cites.
Xu, J., Liu, S., Vahdat, A., Byeon, W., Wang, X., De Mello, S.: Open-vocabulary panoptic segmentation with text-to-image diffusion models. In: CVPR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: ICCV (2023)
2023
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Davtyan, A., Favaro, P.: Learn the force we can: Enabling sparse motion control in multi-object video generation. In: AAAI (2024)
2024
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Geng, D., Owens, A.: Motion guidance: Diffusion-based image editing with differentiable motion estimators. In: ICLR (2024)
2024
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Guo, Y., Yang, C., Rao, A., Liang, Z., Wang, Y., Qiao, Y., Agrawala, M., Lin, D., Dai, B.: Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. In: ICLR (2024)
2024
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Jakab, T., Li, R., Wu, S., Rupprecht, C., Vedaldi, A.: Farm3D: Learning articulated 3d animals by distilling 2d diffusion. In: 3DV (2024)
2024
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Li, J., Tan, H., Zhang, K., Xu, Z., Luan, F., Xu, Y., Hong, Y., Sunkavalli, K., Shakhnarovich, G., Bi, S.: Instant3D: Fast text-to-3D with sparse-view generation and large reconstruction model. In: ICLR (2024)
2024
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Li, Z., Litvak, D., Li, R., Zhang, Y., Jakab, T., Rupprecht, C., Wu, S., Vedaldi, A., Wu, J.: Learning the 3d fauna of the web. In: CVPR (2024)
2024
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Liu, J., Tam, H.I.I., Mahdavi-Amiri, A., Savva, M.: CAGE: controllable articulation generation. In: CVPR (2024)
2024
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Mou, C., Wang, X., Song, J., Shan, Y., Zhang, J.: Dragondiffusion: Enabling drag-style manipulation on diffusion models. In: ICLR (2024)
2024
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Shi, Y., Wang, P., Ye, J., Long, M., Li, K., Yang, X.: MVDream: Multi-view diffusion for 3D generation. In: ICLR (2024)
2024
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Shi, Y., Xue, C., Pan, J., Zhang, W., Tan, V.Y., Bai, S.: Dragdiffusion: Harnessing diffusion models for interactive point-based image editing. In: CVPR (2024)
2024
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Zheng, C., Vedaldi, A.: Free3d: Consistent novel view synthesis without 3d representation. In: CVPR (2024)
2024
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