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This paper introduces a new approach based on a coupled representation and a neural volume optimization to implicitly perform 3D shape editing in latent space.
Differential Coordinates for Interactive Mesh Editing. In Proceedings of IEEE International Conference on Shape Modeling and Applications . 181–190
Yaron Lipman, Olga Sorkine, Daniel Cohen-Or, David Levin, Christian Rossi, and Hans-Peter Seidel. 2004 · 2004
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Olga Sorkine, Daniel Cohen-Or, Yaron Lipman, Marc Alexa, Christian Rössl, and Hans-Peter Seidel. 2004 · 2004
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Mean value coordinates for closed triangular meshes
Tao Ju, Scott Schaefer, and Joe Warren. 2005 · 2005
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Pushkar Joshi, Mark Meyer, Tony DeRose, Brian Green, and Tom Sanocki. 2007 · 2007
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Olga Sorkine and Marc Alexa. 2007 · 2007
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Green coordinates
Yaron Lipman, David Levin, and Daniel Cohen-Or. 2008 · 2008
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iWIRES: an analyze-and-edit approach to shape manipulation
Ran Gal, Olga Sorkine, Niloy J. Mitra, and Daniel Cohen-Or. 2009 · 2009
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Symmetry Hierarchy of Man-Made Objects
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Component-wise Controllers for Structure-Preserving Shape Manipulation
Youyi Zheng, Hongbo Fu, Daniel Cohen-Or, Oscar Kin-Chung Au, and Chiew-Lan Tai. 2011 · 2011
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Structure-aware shape processing. In Eurographics State-of-the-art Report (STAR)
Niloy Mitra, Michael Wand, Hao Zhang, Daniel Cohen-Or, and Martin Bokeloh. 2013 · 2013
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Generative Adversarial Nets. In Conference on Neural Information Processing Systems (NeurIPS) . 2672–2680
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014 · 2014
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ShapeNet: An information-rich 3D model repository
Angel X. Chang, Thomas Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al · 2015
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SMPL: A Skinned Multi-Person Linear model
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. 2015 · 2015
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Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In Conference on Neural Information Processing Systems (NeurIPS) . 82–90
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum. 2016 · 2016
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Improved adversarial systems for 3D object generation and reconstruction. In Conference on Robot Learning . 87–96
Edward J. Smith and David Meger. 2017 · 2017
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Image2StyleGAN: How to embed images into the stylegan latent space?. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 4432–4441
Rameen Abdal, Yipeng Qin, and Peter Wonka. 2019 · 2019
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Learning implicit fields for generative shape modeling. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 5939–5948
Zhiqin Chen and Hao Zhang. 2019 · 2019
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GANalyze: Toward visual definitions of cognitive image properties. In IEEE International Conference on Computer Vision (ICCV) . 5744–5753
Lore Goetschalckx, Alex Andonian, Aude Oliva, and Phillip Isola. 2019 · 2019
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ABC: A big cad model dataset for geometric deep learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 9601–9611
Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo. 2019 · 2019
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Occupancy networks: Learning 3D reconstruction in function space. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 4460–4470
Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019 · 2019
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3DN: 3D Deformation Network. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 1038–1046
Weiyue Wang, Duygu Ceylan, Radomir Mech, and Ulrich Neumann. 2019 · 2019
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BSP-Net: Generating compact meshes via binary space partitioning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 45–54
Zhiqin Chen, Andrea Tagliasacchi, and Hao Zhang. 2020 · 2020
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DualSDF: Semantic shape manipulation using a two-level representation. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 7631–7641
Zekun Hao, Hadar Averbuch-Elor, Noah Snavely, and Serge Belongie. 2020 · 2020
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PointGMM: A neural GMM network for point clouds. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 12054–12063
Amir Hertz, Rana Hanocka, Raja Giryes, and Daniel Cohen-Or. 2020 · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
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Progressive point cloud deconvolution generation network. In European Conference on Computer Vision (ECCV) . 397–413
Le Hui, Rui Xu, Jin Xie, Jianjun Qian, and Jian Yang. 2020 · 2020
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ShapeFlow: Learnable deformation flows among 3D shapes
Chiyu Jiang, Jingwei Huang, Andrea Tagliasacchi, and Leonidas J Guibas. 2020 · 2020
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Interpreting the latent space of GANs for semantic face editing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 9243–9252
Yujun Shen, Jinjin Gu, Xiaoou Tang, and Bolei Zhou. 2020 · 2020
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Unsupervised discovery of interpretable directions in the GAN latent space. In Proceedings of International Conference on Machine Learning (ICML) . 9786–9796
Andrey Voynov and Artem Babenko. 2020 · 2020
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Neural Cages for detail-preserving 3D deformations. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 75–83
Wang Yifan, Noam Aigerman, Vladimir G Kim, Siddhartha Chaudhuri, and Olga Sorkine-Hornung. 2020 · 2020
Point-E: A system for generating 3D point clouds from complex prompts
Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. 2022 · 2022
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DreamFusion: Text-to-3D using 2D Diffusion. In International Conference on Learning Representations (ICLR)
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. 2022 · 2022
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High-resolution image synthesis with latent diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 10684–10695
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022
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LION: Latent Point Diffusion Models for 3D Shape Generation. In Conference on Neural Information Processing Systems (NeurIPS)
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. 2022 · 2022
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Cited alongside, same era.
COALESCE: Component Assembly by Learning to Synthesize Connections. In Proc. of 3DV
Kangxue Yin, Zhiqin Chen, Siddhartha Chaudhuri, Matthew Fisher, Vladimir Kim, and Hao Zhang. 2020 · 2020
Cited alongside, same era.
StyleFlow: Attribute-conditioned exploration of stylegan-generated images using conditional continuous normalizing flows. In ACM Transactions on Graphics (SIGGRAPH) , Vol. 40. 1–21
Rameen Abdal, Peihao Zhu, Niloy J Mitra, and Peter Wonka. 2021 · 2021
Cited alongside, same era.
Navigating the GAN parameter space for semantic image editing. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 3671–3680
Anton Cherepkov, Andrey Voynov, and Artem Babenko. 2021 · 2021
Cited alongside, same era.
Sketch2Mesh: Reconstructing and editing 3D shapes from sketches. In IEEE International Conference on Computer Vision (ICCV) . 13023–13032
Benoit Guillard, Edoardo Remelli, Pierre Yvernay, and Pascal Fua. 2021 · 2021
Cited alongside, same era.
3D Shape Generation With Grid-Based Implicit Functions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 13559–13568
Moritz Ibing, Isaak Lim, and Leif Kobbelt. 2021 · 2021
Cited alongside, same era.
SP-GAN: Sphere-Guided 3D Shape Generation and Manipulation
Ruihui Li, Xianzhi Li, Ke-Hei Hui, and Chi-Wing Fu. 2021 · 2021
Cited alongside, same era.
DeepMetaHandles: Learning deformation meta-handles of 3D meshes with biharmonic coordinates. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 12–21
Minghua Liu, Minhyuk Sung, Radomir Mech, and Hao Su. 2021 · 2021
Cited alongside, same era.
Panos Achlioptas, Ian Huang, Minhyuk Sung, Sergey Tulyakov, and Leonidas Guibas. 2023 · 2023
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InstructPix2Pix: Learning to follow image editing instructions. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 18392–18402
Tim Brooks, Aleksander Holynski, and Alexei A Efros. 2023 · 2023
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Diffusion-SDF: Conditional generative modeling of signed distance functions. In IEEE International Conference on Computer Vision (ICCV) . 2262–2272
Gene Chou, Yuval Bahat, and Felix Heide. 2023 · 2023
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Objaverse-XL: A universe of 10M+ 3D objects
Matt Deitke, Ruoshi Liu, Matthew Wallingford, Huong Ngo, Oscar Michel, Aditya Kusupati, Alan Fan, Christian Laforte, Vikram Voleti, Samir Yitzhak Gadre, et al · 2023
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DragVideo: Interactive Drag-style Video Editing
Yufan Deng, Ruida Wang, Yuhao Zhang, Yu-Wing Tai, and Chi-Keung Tang. 2023 · 2023
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Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation
Jingyu Hu*, Ka-Hei Hui*, Zhengzhe Liu, Ruihui Li, and Chi-Wing Fu. 2023a · 2023
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Imagic: Text-based real image editing with diffusion models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 6007–6017
Bahjat Kawar, Shiran Zada, Oran Lang, Omer Tov, Huiwen Chang, Tali Dekel, Inbar Mosseri, and Michal Irani. 2023 · 2023
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SALAD: Part-level latent diffusion for 3D shape generation and manipulation. In IEEE International Conference on Computer Vision (ICCV) . 14441–14451
Juil Koo, Seungwoo Yoo, Minh Hieu Nguyen, and Minhyuk Sung. 2023 · 2023
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Magic3D: High-resolution text-to-3D content creation. In Conference on Neural Information Processing Systems (NeurIPS) . 300–309
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. 2023 · 2023
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EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
Zhengzhe Liu, Jingyu Hu, Ka-Hei Hui, Xiaojuan Qi, Daniel Cohen-Or, and Chi-Wing Fu. 2023b · 2023
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Controllable Mesh Generation Through Sparse Latent Point Diffusion Models. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . 271–280
Zhaoyang Lyu, Jinyi Wang, Yuwei An, Ya Zhang, Dahua Lin, and Bo Dai. 2023 · 2023
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DragonDiffusion: Enabling drag-style manipulation on diffusion models
Chong Mou, Xintao Wang, Jiechong Song, Ying Shan, and Jian Zhang. 2023 · 2023
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Drag your GAN: Interactive point-based manipulation on the generative image manifold. In Proceedings of SIGGRAPH . 1–11
Xingang Pan, Ayush Tewari, Thomas Leimkühler, Lingjie Liu, Abhimitra Meka, and Christian Theobalt. 2023 · 2023
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Zero-shot image-to-image translation. In Proceedings of SIGGRAPH . 1–11
Gaurav Parmar, Krishna Kumar Singh, Richard Zhang, Yijun Li, Jingwan Lu, and Jun-Yan Zhu. 2023 · 2023
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XCube: Large-Scale 3D Generative Modeling using Sparse Voxel Hierarchies
Xuanchi Ren, Jiahui Huang, Xiaohui Zeng, Ken Museth, Sanja Fidler, and Francis Williams. 2023 · 2023
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DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing
Yujun Shi, Chuhui Xue, Jiachun Pan, Wenqing Zhang, Vincent YF Tan, and Song Bai. 2023 · 2023
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MeshGPT: Generating Triangle Meshes with Decoder-Only Transformers
Yawar Siddiqui, Antonio Alliegro, Alexey Artemov, Tatiana Tommasi, Daniele Sirigatti, Vladislav Rosov, Angela Dai, and Matthias Nießner. 2023 · 2023
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3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
Biao Zhang, Jiapeng Tang, Matthias Nießner, and Peter Wonka. 2023 · 2023
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Locally Attentional SDF Diffusion for Controllable 3D Shape Generation
Xin-Yang Zheng, Hao Pan, Peng-Shuai Wang, Xin Tong, Yang Liu, and Heung-Yeung Shum. 2023 · 2023
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MRGAN: Multi-rooted 3D shape generation with unsupervised part disentanglement. In In ICCV Workshop on Structural and Compositional Learning on 3D Data (StruCo3D). 2039–2048
Rinon Gal, Amit Bermano, Hao Zhang, and Daniel Cohen-Or. 2020 · 2048
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