{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:27:35Z","timestamp":1792081655258,"version":"4.4.0"},"reference-count":65,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T00:00:00Z","timestamp":1731974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Hong Kong Research Grant Council","award":["ECS 27209621"],"award-info":[{"award-number":["ECS 27209621"]}]},{"name":"Hong Kong Research Grant Council","award":["GRF 17202422"],"award-info":[{"award-number":["GRF 17202422"]}]},{"name":"Hong Kong Research Grant Council","award":["GRF 17212923"],"award-info":[{"award-number":["GRF 17212923"]}]},{"name":"Hong Kong Research Grant Council","award":["T45-701\/22-R"],"award-info":[{"award-number":["T45-701\/22-R"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Graph."],"published-print":{"date-parts":[[2024,12,19]]},"abstract":"<jats:p>While high-quality texture maps are essential for realistic 3D asset rendering, few studies have explored learning directly in the texture space, especially on large-scale datasets. In this work, we depart from the conventional approach of relying on pre-trained 2D diffusion models for testtime optimization of 3D textures. Instead, we focus on the fundamental problem of learning in the UV texture space itself. For the first time, we train a large diffusion model capable of directly generating high-resolution texture maps in a feed-forward manner. To facilitate efficient learning in high-resolution UV spaces, we propose a scalable network architecture that interleaves convolutions on UV maps with attention layers on point clouds. Leveraging this architectural design, we train a 700 million parameter diffusion model that can generate UV texture maps guided by text prompts and single-view images. Once trained, our model naturally supports various extended applications, including text-guided texture inpainting, sparse-view texture completion, and text-driven texture synthesis. The code is available at https:\/\/github.com\/CVMI-Lab\/TEXGen.<\/jats:p>","DOI":"10.1145\/3687909","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T15:46:04Z","timestamp":1732031164000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":27,"title":["TEXGen: a Generative Diffusion Model for Mesh Textures"],"prefix":"10.1145","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4531-1254","authenticated-orcid":false,"given":"Xin","family":"Yu","sequence":"first","affiliation":[{"name":"The University of Hong Kong (HKU), Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6673-9670","authenticated-orcid":false,"given":"Ze","family":"Yuan","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6164-8343","authenticated-orcid":false,"given":"Yuan-Chen","family":"Guo","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8293-3223","authenticated-orcid":false,"given":"Ying-Tian","family":"Liu","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0370-4153","authenticated-orcid":false,"given":"Jianhui","family":"Liu","sequence":"additional","affiliation":[{"name":"The University of Hong Kong (HKU), Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6090-3899","authenticated-orcid":false,"given":"Yangguang","family":"Li","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0416-4374","authenticated-orcid":false,"given":"Yan-Pei","family":"Cao","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9774-4687","authenticated-orcid":false,"given":"Ding","family":"Liang","sequence":"additional","affiliation":[{"name":"VAST, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4285-1626","authenticated-orcid":false,"given":"Xiaojuan","family":"Qi","sequence":"additional","affiliation":[{"name":"The University of Hong Kong (HKU), Hong Kong, Hong Kong"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,19]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al.","author":"Achiam Josh","year":"2023","unstructured":"Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023. Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)."},{"key":"e_1_2_1_2_1","unstructured":"Anthropic. 2024. Claude 3.5 Sonnet. https:\/\/www.anthropic.com\/news\/claude-3-5-sonnet Accessed: 2024-08-20."},{"key":"e_1_2_1_3_1","unstructured":"James Betker Gabriel Goh Li Jing Tim Brooks Jianfeng Wang Linjie Li Long Ouyang Juntang Zhuang Joyce Lee Yufei Guo et al. 2023. Improving image generation with better captions. Computer Science. https:\/\/cdn.openai.com\/papers\/dall-e-3.pdf 2 3 (2023) 8."},{"key":"e_1_2_1_4_1","volume-title":"Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets. CoRR abs\/2311.15127","author":"Blattmann Andreas","year":"2023","unstructured":"Andreas Blattmann, Tim Dockhorn, Sumith Kulal, Daniel Mendelevitch, Maciej Kilian, Dominik Lorenz, Yam Levi, Zion English, Vikram Voleti, Adam Letts, Varun Jampani, and Robin Rombach. 2023. Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets. CoRR abs\/2311.15127 (2023)."},{"key":"e_1_2_1_5_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision. 8918--8928","author":"Bokhovkin Alexey","year":"2023","unstructured":"Alexey Bokhovkin, Shubham Tulsiani, and Angela Dai. 2023. Mesh2tex: Generating mesh textures from image queries. In Proceedings of the IEEE\/CVF International Conference on Computer Vision. 8918--8928."},{"key":"e_1_2_1_6_1","volume-title":"TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models","author":"Cao Tianshi","unstructured":"Tianshi Cao, Karsten Kreis, Sanja Fidler, Nicholas Sharp, and Kangxue Yin. 2023. TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models. In ICCV. IEEE, 4146--4158."},{"key":"e_1_2_1_7_1","volume-title":"MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment. arXiv preprint arXiv:2404.02899","author":"Ceylan Duygu","year":"2024","unstructured":"Duygu Ceylan, Valentin Deschaintre, Thibault Groueix, Rosalie Martin, Chun-Hao Huang, Romain Rouffet, Vladimir Kim, and Ga\u00ebtan Lassagne. 2024. MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment. arXiv preprint arXiv:2404.02899 (2024)."},{"key":"e_1_2_1_8_1","volume-title":"Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012","author":"Chang Angel X","year":"2015","unstructured":"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)."},{"key":"e_1_2_1_9_1","volume-title":"Text2Tex: Text-driven Texture Synthesis via Diffusion Models","author":"Chen Dave Zhenyu","year":"1851","unstructured":"Dave Zhenyu Chen, Yawar Siddiqui, Hsin-Ying Lee, Sergey Tulyakov, and Matthias Nie\u00dfner. 2023b. Text2Tex: Text-driven Texture Synthesis via Diffusion Models. In ICCV. IEEE, 18512--18522."},{"key":"e_1_2_1_10_1","volume-title":"ACCV (3) (Lecture Notes in Computer Science","author":"Chen Kevin","unstructured":"Kevin Chen, Christopher B. Choy, Manolis Savva, Angel X. Chang, Thomas A. Funkhouser, and Silvio Savarese. 2018. Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddings. In ACCV (3) (Lecture Notes in Computer Science, Vol. 11363). Springer, 100--116."},{"key":"e_1_2_1_11_1","volume-title":"Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation","author":"Chen Rui","unstructured":"Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. 2023a. Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation. In ICCV. IEEE, 22189--22199."},{"key":"e_1_2_1_12_1","volume-title":"TUVF: Learning Generalizable Texture UV Radiance Fields. CoRR abs\/2305.03040","author":"Cheng An-Chieh","year":"2023","unstructured":"An-Chieh Cheng, Xueting Li, Sifei Liu, and Xiaolong Wang. 2023. TUVF: Learning Generalizable Texture UV Radiance Fields. CoRR abs\/2305.03040 (2023)."},{"key":"e_1_2_1_13_1","volume-title":"Conditional positional encodings for vision transformers. arXiv preprint arXiv:2102.10882","author":"Chu Xiangxiang","year":"2021","unstructured":"Xiangxiang Chu, Zhi Tian, Bo Zhang, Xinlong Wang, and Chunhua Shen. 2021. Conditional positional encodings for vision transformers. arXiv preprint arXiv:2102.10882 (2021)."},{"key":"e_1_2_1_14_1","volume-title":"Alex Birch, Tanishq Mathew Abraham, Daniel Z Kaplan, and Enrico Shippole.","author":"Crowson Katherine","year":"2024","unstructured":"Katherine Crowson, Stefan Andreas Baumann, Alex Birch, Tanishq Mathew Abraham, Daniel Z Kaplan, and Enrico Shippole. 2024. Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers. arXiv preprint arXiv:2401.11605 (2024)."},{"key":"e_1_2_1_15_1","volume-title":"Objaverse: A Universe of Annotated 3D Objects","author":"Deitke Matt","year":"2023","unstructured":"Matt Deitke, Dustin Schwenk, Jordi Salvador, Luca Weihs, Oscar Michel, Eli Vander-Bilt, Ludwig Schmidt, Kiana Ehsani, Aniruddha Kembhavi, and Ali Farhadi. 2023. Objaverse: A Universe of Annotated 3D Objects. In CVPR. IEEE, 13142--13153."},{"key":"e_1_2_1_16_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4620--4629","author":"Gao Chenjian","year":"2024","unstructured":"Chenjian Gao, Boyan Jiang, Xinghui Li, Yingpeng Zhang, and Qian Yu. 2024. GenesisTex: Adapting Image Denoising Diffusion to Texture Space. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4620--4629."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_2_1_18_1","volume-title":"Neubegr\u00fcndung der mathematik. erste mitteilung","author":"Hilbert David","unstructured":"David Hilbert and David Hilbert. 1935. Neubegr\u00fcndung der mathematik. erste mitteilung. Springer."},{"key":"e_1_2_1_19_1","volume-title":"Denoising diffusion probabilistic models. Advances in neural information processing systems 33","author":"Ho Jonathan","year":"2020","unstructured":"Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020. Denoising diffusion probabilistic models. Advances in neural information processing systems 33 (2020), 6840--6851."},{"key":"e_1_2_1_20_1","volume-title":"Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598","author":"Ho Jonathan","year":"2022","unstructured":"Jonathan Ho and Tim Salimans. 2022. Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)."},{"key":"e_1_2_1_21_1","volume-title":"Lrm: Large reconstruction model for single image to 3d. arXiv preprint arXiv:2311.04400","author":"Hong Yicong","year":"2023","unstructured":"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)."},{"key":"e_1_2_1_22_1","unstructured":"Kaiyi Huang Chengqi Duan Kaiyue Sun Enze Xie Zhenguo Li and Xihui Liu. 2023a. T2I-CompBench: A Comprehensive Benchmark for Text-to-Image Models. https:\/\/github.com\/Karine-Huang\/T2I-CompBench GitHub repository."},{"key":"e_1_2_1_23_1","first-page":"78723","article-title":"T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation","volume":"36","author":"Huang Kaiyi","year":"2023","unstructured":"Kaiyi Huang, Kaiyue Sun, Enze Xie, Zhenguo Li, and Xihui Liu. 2023b. T2i-compbench: A comprehensive benchmark for open-world compositional text-to-image generation. Advances in Neural Information Processing Systems 36 (2023), 78723--78747.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_24_1","volume-title":"A Style-Based Generator Architecture for Generative Adversarial Networks","author":"Karras Tero","unstructured":"Tero Karras, Samuli Laine, and Timo Aila. 2019. A Style-Based Generator Architecture for Generative Adversarial Networks. In CVPR. Computer Vision Foundation \/ IEEE, 4401--4410."},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00831"},{"key":"e_1_2_1_26_1","volume-title":"Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214","author":"Li Jiahao","year":"2023","unstructured":"Jiahao Li, Hao Tan, Kai Zhang, Zexiang Xu, Fujun Luan, Yinghao Xu, Yicong Hong, Kalyan Sunkavalli, Greg Shakhnarovich, and Sai Bi. 2023. Instant3d: Fast text-to-3d with sparse-view generation and large reconstruction model. arXiv preprint arXiv:2311.06214 (2023)."},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00037"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/WACV57701.2024.00532"},{"key":"e_1_2_1_29_1","volume-title":"Text-guided texturing by synchronized multi-view diffusion. arXiv preprint arXiv:2311.12891","author":"Liu Yuxin","year":"2023","unstructured":"Yuxin Liu, Minshan Xie, Hanyuan Liu, and Tien-Tsin Wong. 2023. Text-guided texturing by synchronized multi-view diffusion. arXiv preprint arXiv:2311.12891 (2023)."},{"key":"e_1_2_1_30_1","volume-title":"Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures","author":"Metzer Gal","unstructured":"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 CVPR. IEEE, 12663--12673."},{"key":"e_1_2_1_31_1","unstructured":"Guy M Morton. 1966. A computer oriented geodetic data base and a new technique in file sequencing. (1966)."},{"key":"e_1_2_1_32_1","volume-title":"Point-e: A system for generating 3d point clouds from complex prompts. arXiv preprint arXiv:2212.08751","author":"Nichol Alex","year":"2022","unstructured":"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)."},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00463"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00387"},{"key":"e_1_2_1_35_1","volume-title":"Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988","author":"Poole Ben","year":"2022","unstructured":"Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. 2022. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209.14988 (2022)."},{"key":"e_1_2_1_36_1","volume-title":"International conference on machine learning. PMLR, 8748--8763","author":"Radford Alec","year":"2021","unstructured":"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."},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588432.3591503"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"e_1_2_1_39_1","volume-title":"U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference","author":"Ronneberger Olaf","year":"2015","unstructured":"Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention-MICCAI 2015: 18th international conference, Munich, Germany, October 5--9, 2015, proceedings, part III 18. Springer, 234--241."},{"key":"e_1_2_1_40_1","volume-title":"Burcu Karagol Ayan, Tim Salimans, et al.","author":"Saharia Chitwan","year":"2022","unstructured":"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."},{"key":"e_1_2_1_41_1","volume-title":"Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512","author":"Salimans Tim","year":"2022","unstructured":"Tim Salimans and Jonathan Ho. 2022. Progressive distillation for fast sampling of diffusion models. arXiv preprint arXiv:2202.00512 (2022)."},{"key":"e_1_2_1_42_1","volume-title":"Texturify: Generating Textures on 3D Shape Surfaces. arXiv preprint arXiv:2204.02411","author":"Siddiqui Yawar","year":"2022","unstructured":"Yawar Siddiqui, Justus Thies, Fangchang Ma, Qi Shan, Matthias Nie\u00dfner, and Angela Dai. 2022. Texturify: Generating Textures on 3D Shape Surfaces. arXiv preprint arXiv:2204.02411 (2022)."},{"key":"e_1_2_1_43_1","volume-title":"Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502","author":"Song Jiaming","year":"2020","unstructured":"Jiaming Song, Chenlin Meng, and Stefano Ermon. 2020. Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)."},{"key":"e_1_2_1_44_1","unstructured":"Gemini Team Rohan Anil Sebastian Borgeaud Yonghui Wu Jean-Baptiste Alayrac Jiahui Yu Radu Soricut Johan Schalkwyk Andrew M Dai Anja Hauth et al. 2023. Gemini: a family of highly capable multimodal models. arXiv preprint arXiv:2312.11805 (2023)."},{"key":"e_1_2_1_45_1","volume-title":"Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151","author":"Tochilkin Dmitry","year":"2024","unstructured":"Dmitry Tochilkin, David Pankratz, Zexiang Liu, Zixuan Huang, Adam Letts, Yangguang Li, Ding Liang, Christian Laforte, Varun Jampani, and Yan-Pei Cao. 2024. Triposr: Fast 3d object reconstruction from a single image. arXiv preprint arXiv:2403.02151 (2024)."},{"key":"e_1_2_1_46_1","unstructured":"Hugo Touvron Louis Martin Kevin Stone Peter Albert Amjad Almahairi Yasmine Babaei Nikolay Bashlykov Soumya Batra Prajjwal Bhargava Shruti Bhosale et al. 2023. Llama 2: Open foundation and fine-tuned chat models. arXiv preprint arXiv:2307.09288 (2023)."},{"key":"e_1_2_1_47_1","volume-title":"Score Jacobian Chaining: Lifting Pretrained 2D Diffusion Models for 3D Generation","author":"Wang Haochen","unstructured":"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 CVPR. IEEE, 12619--12629."},{"key":"e_1_2_1_48_1","volume-title":"Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction. arXiv preprint arXiv:2311.12024","author":"Wang Peng","year":"2023","unstructured":"Peng Wang, Hao Tan, Sai Bi, Yinghao Xu, Fujun Luan, Kalyan Sunkavalli, Wenping Wang, Zexiang Xu, and Kai Zhang. 2023c. Pf-lrm: Pose-free large reconstruction model for joint pose and shape prediction. arXiv preprint arXiv:2311.12024 (2023)."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3618331"},{"key":"e_1_2_1_50_1","volume-title":"ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. arXiv preprint arXiv:2305.16213","author":"Wang Zhengyi","year":"2023","unstructured":"Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao, Chongxuan Li, Hang Su, and Jun Zhu. 2023b. ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. arXiv preprint arXiv:2305.16213 (2023)."},{"key":"e_1_2_1_51_1","volume-title":"TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization. arXiv preprint arXiv:2403.15009","author":"Wu Jinbo","year":"2024","unstructured":"Jinbo Wu, Xing Liu, Chenming Wu, Xiaobo Gao, Jialun Liu, Xinqi Liu, Chen Zhao, Haocheng Feng, Errui Ding, and Jingdong Wang. 2024. TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization. arXiv preprint arXiv:2403.15009 (2024)."},{"key":"e_1_2_1_52_1","volume-title":"Point transformer v3: Simpler, faster, stronger. arXiv preprint arXiv:2312.10035","author":"Wu Xiaoyang","year":"2023","unstructured":"Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, and Hengshuang Zhao. 2023. Point transformer v3: Simpler, faster, stronger. arXiv preprint arXiv:2312.10035 (2023)."},{"key":"e_1_2_1_53_1","first-page":"33330","article-title":"Point transformer v2: Grouped vector attention and partition-based pooling","volume":"35","author":"Wu Xiaoyang","year":"2022","unstructured":"Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, and Hengshuang Zhao. 2022. Point transformer v2: Grouped vector attention and partition-based pooling. Advances in Neural Information Processing Systems 35 (2022), 33330--33342.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_1_54_1","volume-title":"GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation. CoRR abs\/2403.14621","author":"Xu Yinghao","year":"2024","unstructured":"Yinghao Xu, Zifan Shi, Wang Yifan, Hansheng Chen, Ceyuan Yang, Sida Peng, Yujun Shen, and Gordon Wetzstein. 2024. GRM: Large Gaussian Reconstruction Model for Efficient 3D Reconstruction and Generation. CoRR abs\/2403.14621 (2024)."},{"key":"e_1_2_1_55_1","unstructured":"Yinghao Xu Hao Tan Fujun Luan Sai Bi Peng Wang Jiahao Li Zifan Shi Kalyan Sunkavalli Gordon Wetzstein Zexiang Xu et al. 2023. Dmv3d: Denoising multiview diffusion using 3d large reconstruction model. arXiv preprint arXiv:2311.09217 (2023)."},{"key":"e_1_2_1_56_1","volume-title":"Swin3d: A pretrained transformer backbone for 3d indoor scene understanding. arXiv preprint arXiv:2304.06906","author":"Yang Yu-Qi","year":"2023","unstructured":"Yu-Qi Yang, Yu-Xiao Guo, Jian-Yu Xiong, Yang Liu, Hao Pan, Peng-Shuai Wang, Xin Tong, and Baining Guo. 2023. Swin3d: A pretrained transformer backbone for 3d indoor scene understanding. arXiv preprint arXiv:2304.06906 (2023)."},{"key":"e_1_2_1_57_1","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4304--4314","author":"Yeh Yu-Ying","year":"2024","unstructured":"Yu-Ying Yeh, Jia-Bin Huang, Changil Kim, Lei Xiao, Thu Nguyen-Phuoc, Numair Khan, Cheng Zhang, Manmohan Chandraker, Carl S Marshall, Zhao Dong, et al. 2024. Texturedreamer: Image-guided texture synthesis through geometry-aware diffusion. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 4304--4314."},{"key":"e_1_2_1_58_1","unstructured":"Jonathan Young. 2018. xatlas. https:\/\/github.com\/jpcy\/xatlas."},{"key":"e_1_2_1_59_1","volume-title":"Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 4206--4216","author":"Yu Xin","year":"2023","unstructured":"Xin Yu, Peng Dai, Wenbo Li, Lan Ma, Zhengzhe Liu, and Xiaojuan Qi. 2023a. Texture Generation on 3D Meshes with Point-UV Diffusion. In Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV). 4206--4216."},{"key":"e_1_2_1_60_1","volume-title":"Text-to-3d with classifier score distillation. arXiv preprint arXiv:2310.19415","author":"Yu Xin","year":"2023","unstructured":"Xin Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Song-Hai Zhang, and Xiaojuan Qi. 2023b. Text-to-3d with classifier score distillation. arXiv preprint arXiv:2310.19415 (2023)."},{"key":"e_1_2_1_61_1","volume-title":"Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models. arXiv preprint arXiv:2312.13913","author":"Zeng Xianfang","year":"2023","unstructured":"Xianfang Zeng. 2023. Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models. arXiv preprint arXiv:2312.13913 (2023)."},{"key":"e_1_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.00355"},{"key":"e_1_2_1_63_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00068"},{"key":"e_1_2_1_64_1","volume-title":"ACM SIGGRAPH 2024 Conference Papers. 1--12","author":"Zhang Shangzhan","year":"2024","unstructured":"Shangzhan Zhang, Sida Peng, Tao Xu, Yuanbo Yang, Tianrun Chen, Nan Xue, Yujun Shen, Hujun Bao, Ruizhen Hu, and Xiaowei Zhou. 2024. MaPa: Text-driven Photorealistic Material Painting for 3D Shapes. In ACM SIGGRAPH 2024 Conference Papers. 1--12."},{"key":"e_1_2_1_65_1","volume-title":"Triplane meets gaussian splatting: Fast and generalizable single-view 3d reconstruction with transformers. arXiv preprint arXiv:2312.09147","author":"Zou Zi-Xin","year":"2023","unstructured":"Zi-Xin Zou, Zhipeng Yu, Yuan-Chen Guo, Yangguang Li, Ding Liang, Yan-Pei Cao, and Song-Hai Zhang. 2023. Triplane meets gaussian splatting: Fast and generalizable single-view 3d reconstruction with transformers. arXiv preprint arXiv:2312.09147 (2023)."}],"container-title":["ACM Transactions on Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3687909","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3687909","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:09:57Z","timestamp":1750295397000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3687909"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,19]]},"references-count":65,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,12,19]]}},"alternative-id":["10.1145\/3687909"],"URL":"https:\/\/doi.org\/10.1145\/3687909","relation":{},"ISSN":["0730-0301","1557-7368"],"issn-type":[{"value":"0730-0301","type":"print"},{"value":"1557-7368","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,19]]},"assertion":[{"value":"2024-11-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}