Coin3D Introduces a New Standard for Interactive 3D Asset Generation | HackerNoon

Table of Links

Abstract and 1 Introduction

2 Related Works

3 Method and 3.1 Proxy-Guided 3D Conditioning for Diffusion

3.2 Interactive Generation Workflow and 3.3 Volume Conditioned Reconstruction

4 Experiment and 4.1 Comparison on Proxy-based and Image-based 3D Generation

4.2 Comparison on Controllable 3D Object Generation, 4.3 Interactive Generation with Part Editing & 4.4 Ablation Studies

5 Conclusions, Acknowledgments, and References

SUPPLEMENTARY MATERIAL

A. Implementation Details

B. More Discussions

C. More Experiments

5 CONCLUSIONS

We have proposed a novel 3D assets generation pipeline, named Coin3D. Our method successfully integrates 3D-aware control from coarse shape proxies to the 3D object generation task and enables an interactive generation workflow, where users can freely alter prompts/shapes or regenerate designated local parts, and inspect the changes with interactive 3D preview in a few seconds.

Limitations and future works. First, our workflow starts from synthesizing 2D image candidates, which provides users with quick preview and selection but requires prompt engineering to obtain a clean and satisfactory result without complex background textures. In the future, we can finetune a 2D diffusion model with objectcentric data [Deitke et al. 2023] and introduce LLM-based prompt enhancement [Gustavosta 2023] to handle this issue. Second, due to the limited resolution of the base diffusion model [Liu et al. 2023c], our method cannot produce fine-level details (e.g., complex fur textures or wrinkled surface), which can be further improved by adopting better backbones [Shi et al. 2023a] or taking the refinement stage with high-resolution optimization [Lin et al. 2023; Tang et al. 2023a]. Third, our reconstructed texture meshes already baked lighting effects while lacking PBR materials for modern rendering pipelines. In future work, we can train a material-disentangled diffusion model to enable generating objects with PBR materials.

ACKNOWLEDGMENTS

We would like to acknowledge the support from the NSFC (No. 62102356), Information Technology Center and State Key Lab of CAD&CG, Zhejiang University. We also express our gratitude to all the anonymous reviewers for their professional and constructive comments. The authors from Zhejiang University are also affiliated with the State Key Laboratory of CAD&CG.

REFERENCES

Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas. 2018. Learning representations and generative models for 3d point clouds. In International conference on machine learning. PMLR, 40–49.

Chong Bao, Yinda Zhang, Yuan Li, Xiyu Zhang, Bangbang Yang, Hujun Bao, Marc Pollefeys, Guofeng Zhang, and Zhaopeng Cui. 2024. GeneAvatar: Generic ExpressionAware Volumetric Head Avatar Editing from a Single Image. arXiv preprint arXiv:2404.02152 (2024).

Chong Bao, Yinda Zhang, Bangbang Yang, Tianxing Fan, Zesong Yang, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui. 2023. 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. 20919–20929.

Omer Bar-Tal, Lior Yariv, Yaron Lipman, and Tali Dekel. 2023. Multidiffusion: Fusing diffusion paths for controlled image generation. (2023).

Shariq Farooq Bhat, Niloy J Mitra, and Peter Wonka. 2023. LooseControl: Lifting ControlNet for Generalized Depth Conditioning. arXiv preprint arXiv:2312.03079 (2023).

Ollin Boer Bohan. 2023. Tiny AutoEncoder for Stable Diffusion. https://github.com/ madebyollin/taesd. Accessed: 2023-10-03.

Eric R Chan, Connor Z Lin, Matthew A Chan, Koki Nagano, Boxiao Pan, Shalini De Mello, Orazio Gallo, Leonidas J Guibas, Jonathan Tremblay, Sameh Khamis, et al. 2022. Efficient geometry-aware 3d generative adversarial networks. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 16123–16133.

Eric R Chan, Marco Monteiro, Petr Kellnhofer, Jiajun Wu, and Gordon Wetzstein. 2021. pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5799–5809.

Angel X Chang, Thomas Funkhouser, Leonidas Guibas, Pat Hanrahan, Qixing Huang, Zimo Li, Silvio Savarese, Manolis Savva, Shuran Song, Hao Su, et al. 2015. Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015).

Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. 2023a. Fantasia3d: Disentangling geometry and appearance for high-quality text-to-3d content creation. arXiv preprint arXiv:2303.13873 (2023).

Yang Chen, Yingwei Pan, Yehao Li, Ting Yao, and Tao Mei. 2023b. Control3d: Towards controllable text-to-3d generation. In Proceedings of the 31st ACM International Conference on Multimedia. 1148–1156.

Yuedong Chen, Qianyi Wu, Chuanxia Zheng, Tat-Jen Cham, and Jianfei Cai. 2022. Sem2nerf: Converting single-view semantic masks to neural radiance fields. In European Conference on Computer Vision. Springer, 730–748.

Xinhua Cheng, Tianyu Yang, Jianan Wang, Yu Li, Lei Zhang, Jian Zhang, and Li Yuan. 2023b. Progressive3d: Progressively local editing for text-to-3d content creation with complex semantic prompts. arXiv preprint arXiv:2310.11784 (2023).

Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander G Schwing, and LiangYan Gui. 2023a. Sdfusion: Multimodal 3d shape completion, reconstruction, and generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4456–4465.

Christopher B Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, and Silvio Savarese. 2016. 3d-r2n2: A unified approach for single and multi-view 3d object reconstruction. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VIII 14. Springer, 628–644.

Dana Cohen-Bar, Elad Richardson, Gal Metzer, Raja Giryes, and Daniel Cohen-Or. 2023. Set-the-scene: Global-local training for generating controllable nerf scenes. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 2920–2929.

Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli VanderBilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. 2023. Objaverse: A universe of annotated 3d objects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13142–13153.

Kangle Deng, Gengshan Yang, Deva Ramanan, and Jun-Yan Zhu. 2023. 3d-aware conditional image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 4434–4445.

Anastasia Dubrovina, Fei Xia, Panos Achlioptas, Mira Shalah, Raphaël Groscot, and Leonidas J Guibas. 2019. Composite shape modeling via latent space factorization. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 8140– 8149.

Arpad E Elo. 1967. The proposed uscf rating system, its development, theory, and applications. Chess life 22, 8 (1967), 242–247.

Dave Epstein, Taesung Park, Richard Zhang, Eli Shechtman, and Alexei A Efros. 2022. Blobgan: Spatially disentangled scene representations. In European Conference on Computer Vision. Springer, 616–635.

Haoqiang Fan, Hao Su, and Leonidas J Guibas. 2017. A point set generation network for 3d object reconstruction from a single image. In Proceedings of the IEEE conference on computer vision and pattern recognition. 605–613.

Jun Gao, Tianchang Shen, Zian Wang, Wenzheng Chen, Kangxue Yin, Daiqing Li, Or Litany, Zan Gojcic, and Sanja Fidler. 2022. Get3d: A generative model of high quality 3d textured shapes learned from images. Advances In Neural Information Processing Systems 35 (2022), 31841–31854.

Thibault Groueix, Matthew Fisher, Vladimir G Kim, Bryan C Russell, and Mathieu Aubry. 2018. A papier-mâché approach to learning 3d surface generation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 216– 224.

Jiatao Gu, Lingjie Liu, Peng Wang, and Christian Theobalt. 2021. Stylenerf: A stylebased 3d-aware generator for high-resolution image synthesis. arXiv preprint arXiv:2110.08985 (2021).

Yuwei Guo, Ceyuan Yang, Anyi Rao, Yaohui Wang, Yu Qiao, Dahua Lin, and Bo Dai. 2023. Animatediff: Animate your personalized text-to-image diffusion models without specific tuning. arXiv preprint arXiv:2307.04725 (2023).

Gustavosta. 2023. MagicPrompt. https://huggingface.co/Gustavosta/MagicPromptStable-Diffusion. Accessed: 2023-10-03.

Zekun Hao, Arun Mallya, Serge Belongie, and Ming-Yu Liu. 2021. Gancraft: Unsupervised 3d neural rendering of minecraft worlds. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 14072–14082.

Ayaan Haque, Matthew Tancik, Alexei A Efros, Aleksander Holynski, and Angjoo Kanazawa. 2023. Instruct-nerf2nerf: Editing 3d scenes with instructions. arXiv preprint arXiv:2303.12789 (2023).

Yuze He, Yushi Bai, Matthieu Lin, Wang Zhao, Yubin Hu, Jenny Sheng, Ran Yi, Juanzi Li, and Yong-Jin Liu. 2023. T3Bench: Benchmarking Current Progress in Text-to-3D Generation. arXiv:2310.02977 [cs.CV]

Fangzhou Hong, Mingyuan Zhang, Liang Pan, Zhongang Cai, Lei Yang, and Ziwei Liu. 2022. Avatarclip: Zero-shot text-driven generation and animation of 3d avatars. arXiv preprint arXiv:2205.08535 (2022).

Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. 2023. Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400 (2023).

Yuming Jiang, Shuai Yang, Haonan Qiu, Wayne Wu, Chen Change Loy, and Ziwei Liu. 2022. Text2human: Text-driven controllable human image generation. ACM Transactions on Graphics (TOG) 41, 4 (2022), 1–11.

Heewoo Jun and Alex Nichol. 2023. Shap-e: Generating conditional 3d implicit functions. arXiv preprint arXiv:2305.02463 (2023).

Hiromichi Kamata, Yuiko Sakuma, Akio Hayakawa, Masato Ishii, and Takuya Narihira. 2023. Instruct 3D-to-3D: Text Instruction Guided 3D-to-3D conversion. arXiv preprint arXiv:2303.15780 (2023).

Angjoo Kanazawa, Shubham Tulsiani, Alexei A Efros, and Jitendra Malik. 2018. Learning category-specific mesh reconstruction from image collections. In Proceedings of the European Conference on Computer Vision (ECCV). 371–386.

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al. 2023. Segment anything. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 4015–4026.

Florian Kluger, Hanno Ackermann, Eric Brachmann, Michael Ying Yang, and Bodo Rosenhahn. 2021. Cuboids revisited: Learning robust 3d shape fitting to single rgb images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 13070–13079.

Muheng Li, Yueqi Duan, Jie Zhou, and Jiwen Lu. 2023b. Diffusion-sdf: Text-to-shape via voxelized diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12642–12651.

Yuhan Li, Yishun Dou, Yue Shi, Yu Lei, Xuanhong Chen, Yi Zhang, Peng Zhou, and Bingbing Ni. 2023a. Focaldreamer: Text-driven 3d editing via focal-fusion assembly. arXiv preprint arXiv:2308.10608 (2023).

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. Magic3d: Highresolution text-to-3d content creation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 300–309.

Minghua Liu, Ruoxi Shi, Linghao Chen, Zhuoyang Zhang, Chao Xu, Xinyue Wei, Hansheng Chen, Chong Zeng, Jiayuan Gu, and Hao Su. 2023b. One-2-3-45++: Fast single image to 3d objects with consistent multi-view generation and 3d diffusion. arXiv preprint arXiv:2311.07885 (2023).

Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Zexiang Xu, Hao Su, et al. 2023d. One2-3-45: Any single image to 3d mesh in 45 seconds without per-shape optimization. arXiv preprint arXiv:2306.16928 (2023).

Ruoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov, Sergey Zakharov, and Carl Vondrick. 2023c. Zero-1-to-3: Zero-shot one image to 3d object. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 9298–9309.

Yuan Liu, Cheng Lin, Zijiao Zeng, Xiaoxiao Long, Lingjie Liu, Taku Komura, and Wenping Wang. 2023a. SyncDreamer: Generating Multiview-consistent Images from a Single-view Image. arXiv preprint arXiv:2309.03453 (2023).

Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, et al. 2023. Wonder3d: Single image to 3d using cross-domain diffusion. arXiv preprint arXiv:2310.15008 (2023).

Andreas Lugmayr, Martin Danelljan, Andres Romero, Fisher Yu, Radu Timofte, and Luc Van Gool. 2022. Repaint: Inpainting using denoising diffusion probabilistic models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11461–11471.

Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, and Andrea Vedaldi. 2023. Realfusion: 360deg reconstruction of any object from a single image. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 8446–8455.

Andrew Melnik, Maksim Miasayedzenkau, Dzianis Makaravets, Dzianis Pirshtuk, Eren Akbulut, Dennis Holzmann, Tarek Renusch, Gustav Reichert, and Helge Ritter. 2024. Face generation and editing with stylegan: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence (2024).

Chenlin Meng, Yutong He, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon. 2021. Sdedit: Guided image synthesis and editing with stochastic differential equations. arXiv preprint arXiv:2108.01073 (2021).

Lars Mescheder, Michael Oechsle, Michael Niemeyer, Sebastian Nowozin, and Andreas Geiger. 2019. Occupancy networks: Learning 3d reconstruction in function space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 4460–4470.

Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes, and Daniel Cohen-Or. 2023. Latent-nerf for shape-guided generation of 3d shapes and textures. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12663–12673.

Chong Mou, Xintao Wang, Liangbin Xie, Jian Zhang, Zhongang Qi, Ying Shan, and Xiaohu Qie. 2023. T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models. arXiv preprint arXiv:2302.08453 (2023).

Charlie Nash, Yaroslav Ganin, SM Ali Eslami, and Peter Battaglia. 2020. Polygen: An autoregressive generative model of 3d meshes. In International conference on machine learning. PMLR, 7220–7229.

Swiya Nath and Dénes Szücs. 2014. Construction play and cognitive skills associated with the development of mathematical abilities in 7-year-old children. Learning and instruction 32 (2014), 73–80.

Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. 2022. Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751 (2022).

Karran Pandey, Paul Guerrero, Matheus Gadelha, Yannick Hold-Geoffroy, Karan Singh, and Niloy Mitra. 2023. Diffusion Handles: Enabling 3D Edits for Diffusion Models by Lifting Activations to 3D. arXiv preprint arXiv:2312.02190 (2023).

Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019. Deepsdf: Learning continuous signed distance functions for shape representation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 165–174.

Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. 2022. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988 (2022).

Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren, Aliaksandr Siarohin, Bing Li, Hsin-Ying Lee, Ivan Skorokhodov, Peter Wonka, Sergey Tulyakov, et al. 2023. Magic123: One image to high-quality 3d object generation using both 2d and 3d diffusion priors. arXiv preprint arXiv:2306.17843 (2023).

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning. PMLR, 8748–8763.

Alec Radford, Karthik Narasimhan, Tim Salimans, Ilya Sutskever, et al. 2018. Improving language understanding by generative pre-training. (2018).

Amit Raj, Srinivas Kaza, Ben Poole, Michael Niemeyer, Nataniel Ruiz, Ben Mildenhall, Shiran Zada, Kfir Aberman, Michael Rubinstein, Jonathan Barron, et al. 2023. Dreambooth3d: Subject-driven text-to-3d generation. arXiv preprint arXiv:2303.13508 (2023).

Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125 1, 2 (2022), 3.

Elad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes, and Daniel Cohen-Or. 2023. TEXTure: Text-guided texturing of 3d shapes. arXiv preprint arXiv:2302.01721 (2023).

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022. High-Resolution Image Synthesis With Latent Diffusion Models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 10684–10695.

Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily L Denton, Kamyar Ghasemipour, Raphael Gontijo Lopes, Burcu Karagol Ayan, Tim Salimans, et al. 2022. Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems 35 (2022), 36479– 36494.

Aditya Sanghi, Hang Chu, Joseph G Lambourne, Ye Wang, Chin-Yi Cheng, Marco Fumero, and Kamal Rahimi Malekshan. 2022. Clip-forge: Towards zero-shot textto-shape generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18603–18613.

Junyoung Seo, Wooseok Jang, Min-Seop Kwak, Jaehoon Ko, Hyeonsu Kim, Junho Kim, Jin-Hwa Kim, Jiyoung Lee, and Seungryong Kim. 2023. Let 2d diffusion model know 3d-consistency for robust text-to-3d generation. arXiv preprint arXiv:2303.07937 (2023).

Tianchang Shen, Jun Gao, Kangxue Yin, Ming-Yu Liu, and Sanja Fidler. 2021. Deep marching tetrahedra: a hybrid representation for high-resolution 3d shape synthesis. Advances in Neural Information Processing Systems 34 (2021), 6087–6101.

Ruoxi Shi, Hansheng Chen, Zhuoyang Zhang, Minghua Liu, Chao Xu, Xinyue Wei, Linghao Chen, Chong Zeng, and Hao Su. 2023a. Zero123++: a single image to consistent multi-view diffusion base model. arXiv preprint arXiv:2310.15110 (2023).

Yichun Shi, Peng Wang, Jianglong Ye, Long Mai, Kejie Li, and Xiao Yang. 2023b. MVDream: Multi-view Diffusion for 3D Generation. arXiv:2308.16512 (2023).

Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang, and Peter Wonka. 2022. Epigraf: Rethinking training of 3d gans. Advances in Neural Information Processing Systems 35 (2022), 24487–24501.

Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020).

Jiaxiang Tang, Jiawei Ren, Hang Zhou, Ziwei Liu, and Gang Zeng. 2023a. Dreamgaussian: Generative gaussian splatting for efficient 3d content creation. arXiv preprint arXiv:2309.16653 (2023).

Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. 2023b. Make-it-3d: High-fidelity 3d creation from a single image with diffusion prior. arXiv preprint arXiv:2303.14184 (2023).

Haochen Wang, Xiaodan Du, Jiahao Li, Raymond A Yeh, and Greg Shakhnarovich. 2023a. Score jacobian chaining: Lifting pretrained 2d diffusion models for 3d generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 12619–12629.

Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Wei Liu, and Yu-Gang Jiang. 2018. Pixel2mesh: Generating 3d mesh models from single rgb images. In Proceedings of the European conference on computer vision (ECCV). 52–67.

Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. 2021. Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction. arXiv preprint arXiv:2106.10689 (2021).

Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. 2023b. PF-LRM: Pose-Free Large Reconstruction Model for Joint Pose and Shape Prediction. arXiv preprint arXiv:2311.12024 (2023).

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. 2022. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems 35 (2022), 24824–24837.

Jiajun Wu, Yifan Wang, Tianfan Xue, Xingyuan Sun, Bill Freeman, and Josh Tenenbaum. 2017. Marrnet: 3d shape reconstruction via 2.5 d sketches. Advances in neural information processing systems 30 (2017).

Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, and Gordon Wetzstein. 2024. GPT-4V (ision) is a Human-Aligned Evaluator for Text-to-3D Generation. arXiv preprint arXiv:2401.04092 (2024).

Haozhe Xie, Hongxun Yao, Xiaoshuai Sun, Shangchen Zhou, and Shengping Zhang. 2019. Pix2vox: Context-aware 3d reconstruction from single and multi-view images. In Proceedings of the IEEE/CVF international conference on computer vision. 2690– 2698.

Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, and Yuxiao Dong. 2023a. ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. arXiv:2304.05977 [cs.CV]

Jiale Xu, Xintao Wang, Weihao Cheng, Yan-Pei Cao, Ying Shan, Xiaohu Qie, and Shenghua Gao. 2023c. Dream3d: Zero-shot text-to-3d synthesis using 3d shape prior and text-to-image diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 20908–20918.

Yinghao Xu, Hao Tan, Fujun Luan, Sai Bi, Peng Wang, Jiahao Li, Zifan Shi, Kalyan Sunkavalli, Gordon Wetzstein, Zexiang Xu, et al. 2023b. Dmv3d: Denoising multiview diffusion using 3d large reconstruction model. arXiv preprint arXiv:2311.09217 (2023).

Bangbang Yang, Chong Bao, Junyi Zeng, Hujun Bao, Yinda Zhang, Zhaopeng Cui, and Guofeng Zhang. 2022a. Neumesh: Learning disentangled neural mesh-based implicit field for geometry and texture editing. In European Conference on Computer Vision. Springer, 597–614.

Bangbang Yang, Wenqi Dong, Lin Ma, Wenbo Hu, Xiao Liu, Zhaopeng Cui, and Yuewen Ma. 2024. Dreamspace: Dreaming your room space with text-driven panoramic texture propagation. In 2024 IEEE Conference Virtual Reality and 3D User Interfaces (VR). IEEE, 650–660.

Bangbang Yang, Yinda Zhang, Yijin Li, Zhaopeng Cui, Sean Fanello, Hujun Bao, and Guofeng Zhang. 2022b. Neural rendering in a room: amodal 3d understanding and free-viewpoint rendering for the closed scene composed of pre-captured objects. ACM Transactions on Graphics (TOG) 41, 4 (2022), 1–10.

Bangbang Yang, Yinda Zhang, Yinghao Xu, Yijin Li, Han Zhou, Hujun Bao, Guofeng Zhang, and Zhaopeng Cui. 2021. Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 13779–13788.

Yao Yao, Zixin Luo, Shiwei Li, Tian Fang, and Long Quan. 2018. Mvsnet: Depth inference for unstructured multi-view stereo. In Proceedings of the European conference on computer vision (ECCV). 767–783.

Wang Yu, Xuelin Qian, Jingyang Huo, Tiejun Huang, Bo Zhao, and Yanwei Fu. 2023. Pushing the Limits of 3D Shape Generation at Scale. arXiv preprint arXiv:2306.11510 (2023).

Lvmin Zhang, Anyi Rao, and Maneesh Agrawala. 2023. Adding conditional control to text-to-image diffusion models. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 3836–3847.

Xingchen Zhou, Ying He, F Richard Yu, Jianqiang Li, and You Li. 2023. RePaintNeRF: NeRF Editting via Semantic Masks and Diffusion Models. arXiv preprint arXiv:2306.05668 (2023).

---