Fetching the paper…
Reading the bibliography…
With the rising industrial attention to 3D virtual modeling technology, generating novel 3D content based on specified conditions (e.g.
Auto-encoding variational bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
Earlier work this paper cites.
ShapeNet: An information-rich 3D model repository
Angel X. Chang, Thomas A. Funkhouser, Leonidas J. Guibas, Pat Hanrahan, Qi-Xing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, Jianxiong Xiao, Li Yi, and Fisher Yu · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
Earlier work this paper cites.
Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
Earlier work this paper cites.
3D ShapeNets: A deep representation for volumetric shapes
Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao · 2015
Earlier work this paper cites.
3D U-Net: Learning dense volumetric segmentation from sparse annotation
Özgün Çiçek, Ahmed Abdulkadir, Soeren S. Lienkamp, Thomas Brox, and Olaf Ronneberger · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
Jiajun Wu, Chengkai Zhang, Tianfan Xue, Bill Freeman, and Josh Tenenbaum · 2016
Earlier work this paper cites.
Perspective transformer nets: Learning single-view 3D object reconstruction without 3D supervision
Xinchen Yan, Jimei Yang, Ersin Yumer, Yijie Guo, and Honglak Lee · 2016
Earlier work this paper cites.
Shape completion using 3D-encoder-predictor CNNs and shape synthesis
Angela Dai, Charles Ruizhongtai Qi, and Matthias Nießner · 2017
Earlier work this paper cites.
Pointnet: Deep learning on point sets for 3D classification and segmentation
Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
Earlier work this paper cites.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J. Guibas · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Learning representations and generative models for 3D point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas J. Guibas · 2018
Earlier work this paper cites.
Text2Shape: Generating shapes from natural language by learning joint embeddings
Kevin Chen, Christopher B. Choy, Manolis Savva, Angel X. Chang, Thomas A. Funkhouser, and Silvio Savarese · 2018
Earlier work this paper cites.
A papier-mâché approach to learning 3D surface generation
Thibault Groueix, Matthew Fisher, Vladimir G. Kim, Bryan C. Russell, and Mathieu Aubry · 2018
Earlier work this paper cites.
Neural 3D mesh renderer
Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada · 2018
Earlier work this paper cites.
Implicit maximum likelihood estimation
Ke Li and Jitendra Malik · 2018
Earlier work this paper cites.
Shapeglot: Learning language for shape differentiation
Panos Achlioptas, Leonidas J. Guibas, Noah D. Goodman, Judy Fan, and Robert X. D. Hawkins · 2019
Cited alongside, same era.
Learning implicit fields for generative shape modeling
Zhiqin Chen and Hao Zhang · 2019
Cited alongside, same era.
Occupancy networks: Learning 3D reconstruction in function space
Lars M. Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger · 2019
Cited alongside, same era.
Deepsdf: Learning continuous signed distance functions for shape representation
Jeong Joon Park, Peter Florence, Julian Straub, Richard A. Newcombe, and Steven Lovegrove · 2019
Cited alongside, same era.
DISN: deep implicit surface network for high-quality single-view 3D reconstruction
Qiangeng Xu, Weiyue Wang, Duygu Ceylan, Radomír Mech, and Ulrich Neumann · 2019
Cited alongside, same era.
Pointflow: 3D point cloud generation with continuous normalizing flows
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Later among the works it cites.
Zero-shot text-to-image generation
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever · 2021
Later among the works it cites.
Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2021
Later among the works it cites.
Pointr: Diverse point cloud completion with geometry-aware transformers
Xumin Yu, Yongming Rao, Ziyi Wang, Zuyan Liu, Jiwen Lu, and Jie Zhou · 2021
Later among the works it cites.
3D shape generation and completion through point-voxel diffusion
Linqi Zhou, Yilun Du, and Jiajun Wu · 2021
Later among the works it cites.
Ldedit: Towards generalized text guided image manipulation via latent diffusion models
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge J. Belongie, and Bharath Hariharan · 2019
Cited alongside, same era.
Deep local shapes: Learning local SDF priors for detailed 3D reconstruction
Rohan Chabra, Jan Eric Lenssen, Eddy Ilg, Tanner Schmidt, Julian Straub, Steven Lovegrove, and Richard A. Newcombe · 2020
Cited alongside, same era.
Curriculum deepsdf
Yueqi Duan, Haidong Zhu, He Wang, Li Yi, Ram Nevatia, and Leonidas J. Guibas · 2020
Cited alongside, same era.
Generative adversarial networks
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, and Yoshua Bengio · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
Cited alongside, same era.
Local implicit grid representations for 3D scenes
Chiyu ”Max” Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, and Thomas A. Funkhouser · 2020
Cited alongside, same era.
SDFDiff: Differentiable rendering of signed distance fields for 3D shape optimization
Yue Jiang, Dantong Ji, Zhizhong Han, and Matthias Zwicker · 2020
Cited alongside, same era.
Paramanand Chandramouli and Kanchana Vaishnavi Gandikota · 2022
Closest in time.
Diffusion models in vision: A survey
Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah · 2022
Closest in time.
GET3D: A generative model of high quality 3D textured shapes learned from images
Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler · 2022
Closest in time.
Diffusionclip: Text-guided diffusion models for robust image manipulation
Gwanghyun Kim, Taesung Kwon, and Jong Chul Ye · 2022
Closest in time.
Towards implicit text-guided 3D shape generation
Zhengzhe Liu, Yi Wang, Xiaojuan Qi, and Chi-Wing Fu · 2022
Closest in time.
Repaint: Inpainting using denoising diffusion probabilistic models
Andreas Lugmayr, Martin Danelljan, Andrés Romero, Fisher Yu, Radu Timofte, and Luc Van Gool · 2022
Closest in time.
Text2mesh: Text-driven neural stylization for meshes
Oscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim, and Rana Hanocka · 2022
Closest in time.
Autosdf: Shape priors for 3D completion, reconstruction and generation
Paritosh Mittal, Yen-Chi Cheng, Maneesh Singh, and Shubham Tulsiani · 2022
Closest in time.
GLIDE: towards photorealistic image generation and editing with text-guided diffusion models
Alexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen · 2022
Closest in time.
Hierarchical text-conditional image generation with CLIP latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
Closest in time.
High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
Closest in time.
Photorealistic text-to-image diffusion models with deep language understanding
Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S. Sara Mahdavi, Rapha Gontijo Lopes, Tim Salimans, Jonathan Ho, David J. Fleet, and Mohammad Norouzi · 2022
Closest in time.
Clip-forge: Towards zero-shot text-to-shape generation
Aditya Sanghi, Hang Chu, Joseph G. Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, and Kamal Rahimi Malekshan · 2022
Closest in time.
LION: latent point diffusion models for 3D shape generation
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis · 2022
Closest in time.