“CharacterGen: Efficient 3D Character Generation From Single Images With Multi-view Pose Canonicalization”
Conference:
Type(s):
Title:
- CharacterGen: Efficient 3D Character Generation From Single Images With Multi-view Pose Canonicalization
Presenter(s)/Author(s):
Abstract:
CharacterGen introduces a streamlined 3D character generation pipeline, which can generate high-quality canonicalized characters from given images within one minute. Additionally, we have curated a dataset, Anime3D, which renders anime characters in multiple poses and views to train and evaluate our model.
References:
[1]
actorcore. 2023. accurig, a software for automatic character rigging. https://actorcore.reallusion.com/auto-rig/accurig
[2]
Thiemo Alldieck, Hongyi Xu, and Cristian Sminchisescu. 2021. imGHUM: Implicit Generative Models of 3D Human Shape and Articulated Pose. In 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021, Montreal, QC, Canada, October 10–17, 2021. IEEE, 5441–5450.
[3]
Kalyan Vasudev Alwala, Abhinav Gupta, and Shubham Tulsiani. 2022. Pretrain, Self-train, Distill: A simple recipe for Supersizing 3D Reconstruction. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18–24, 2022. IEEE, 3763–3772.
[4]
Yukang Cao, Yan-Pei Cao, Kai Han, Ying Shan, and Kwan-Yee K. Wong. 2023. DreamAvatar: Text-and-Shape Guided 3D Human Avatar Generation via Diffusion Models. CoRR abs/2304.00916 (2023). arXiv:2304.00916
[5]
Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2021. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Trans. Pattern Anal. Mach. Intell. 43, 1 (2021), 172–186.
[6]
Sihun Cha, Kwanggyoon Seo, Amirsaman Ashtari, and Junyong Noh. 2023. Generating Texture for 3D Human Avatar from a Single Image using Sampling and Refinement Networks. Comput. Graph. Forum 42, 2 (2023), 385–396.
[7]
Rui Chen, Yongwei Chen, Ningxin Jiao, and Kui Jia. 2023a. Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation. CoRR abs/2303.13873 (2023). arXiv:2303.13873
[8]
Shuhong Chen, Kevin Zhang, Yichun Shi, Heng Wang, Yiheng Zhu, Guoxian Song, Sizhe An, Janus Kristjansson, Xiao Yang, and Matthias Zwicker. 2023b. PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 21068–21077.
[9]
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 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 13142–13153.
[10]
Congyue Deng, Chiyu Max Jiang, Charles R. Qi, Xinchen Yan, Yin Zhou, Leonidas J. Guibas, and Dragomir Anguelov. 2023. NeRDi: Single-View NeRF Synthesis with Language-Guided Diffusion as General Image Priors. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 20637–20647.
[11]
Ziya Erko?, Fangchang Ma, Qi Shan, Matthias Nie?ner, and Angela Dai. 2023. Hyper-Diffusion: Generating Implicit Neural Fields with Weight-Space Diffusion. CoRR abs/2303.17015 (2023). arXiv:2303.17015
[12]
HakuyaLabs. 2023. warudo, a 3D virtual image live broadcast software. https://warudo.app/
[13]
Fangzhou Hong, Zhaoxi Chen, Yushi Lan, Liang Pan, and Ziwei Liu. 2023a. EVA3D: Compositional 3D Human Generation from 2D Image Collections. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1–5, 2023. OpenReview.net. https://openreview.net/pdf?id=g7U9jD_2CUr
[14]
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. ACM Trans. Graph. 41, 4 (2022), 161:1–161:19.
[15]
Yicong Hong, Kai Zhang, Jiuxiang Gu, Sai Bi, Yang Zhou, Difan Liu, Feng Liu, Kalyan Sunkavalli, Trung Bui, and Hao Tan. 2023b. LRM: Large Reconstruction Model for Single Image to 3D. CoRR abs/2311.04400 (2023). arXiv:2311.04400
[16]
Li Hu, Xin Gao, Peng Zhang, Ke Sun, Bang Zhang, and Liefeng Bo. 2023. Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation. CoRR abs/2311.17117 (2023). arXiv:2311.17117
[17]
Yukun Huang, Jianan Wang, Ailing Zeng, He Cao, Xianbiao Qi, Yukai Shi, Zheng-Jun Zha, and Lei Zhang. 2023a. DreamWaltz: Make a Scene with Complex 3D Animatable Avatars. CoRR abs/2305.12529 (2023). arXiv:2305.12529
[18]
Yangyi Huang, Hongwei Yi, Yuliang Xiu, Tingting Liao, Jiaxiang Tang, Deng Cai, and Justus Thies. 2023b. TeCH: Text-guided Reconstruction of Lifelike Clothed Humans. CoRR abs/2308.08545 (2023). arXiv:2308.08545
[19]
Ka-Hei Hui, Ruihui Li, Jingyu Hu, and Chi-Wing Fu. 2022. Neural Wavelet-domain Diffusion for 3D Shape Generation. In SIGGRAPH Asia 2022 Conference Papers, SA 2022, Daegu, Republic of Korea, December 6–9, 2022, Soon Ki Jung, Jehee Lee, and Adam W. Bargteil (Eds.). ACM, 24:1–24:9.
[20]
Ruixiang Jiang, Can Wang, Jingbo Zhang, Menglei Chai, Mingming He, Dongdong Chen, and Jing Liao. 2023. AvatarCraft: Transforming Text into Neural Human Avatars with Parameterized Shape and Pose Control. CoRR abs/2303.17606 (2023). arXiv:2303.17606
[21]
Heewoo Jun and Alex Nichol. 2023. Shap-E: Generating Conditional 3D Implicit Functions. CoRR abs/2305.02463 (2023). arXiv:2305.02463
[22]
Nikos Kolotouros, Thiemo Alldieck, Andrei Zanfir, Eduard Gabriel Bazavan, Mihai Fieraru, and Cristian Sminchisescu. 2023. DreamHuman: Animatable 3D Avatars from Text. CoRR abs/2306.09329 (2023). arXiv:2306.09329
[23]
Samuli Laine, Janne Hellsten, Tero Karras, Yeongho Seol, Jaakko Lehtinen, and Timo Aila. 2020. Modular primitives for high-performance differentiable rendering. ACM Trans. Graph. 39, 6 (2020), 194:1–194:14.
[24]
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. CoRR abs/2311.06214 (2023). arXiv:2311.06214
[25]
Tianye Li, Timo Bolkart, Michael J. Black, Hao Li, and Javier Romero. 2017. Learning a model of facial shape and expression from 4D scans. ACM Trans. Graph. 36, 6 (2017), 194:1–194:17.
[26]
Tingting Liao, Hongwei Yi, Yuliang Xiu, Jiaxiang Tang, Yangyi Huang, Justus Thies, and Michael J. Black. 2023. TADA! Text to Animatable Digital Avatars. CoRR abs/2308.10899 (2023). arXiv:2308.10899
[27]
Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng, Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Liu, and Tsung-Yi Lin. 2023a. Magic3D: HighResolution Text-to-3D Content Creation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 300–309.
[28]
Shanchuan Lin, Bingchen Liu, Jiashi Li, and Xiao Yang. 2023b. Common Diffusion Noise Schedules and Sample Steps are Flawed. CoRR abs/2305.08891 (2023). arXiv:2305.08891
[29]
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. CoRR abs/2311.07885 (2023). arXiv:2311.07885
[30]
Minghua Liu, Chao Xu, Haian Jin, Linghao Chen, Mukund Varma T, Zexiang Xu, and Hao Su. 2023d. One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization. CoRR abs/2306.16928 (2023). arXiv:2306.16928
[31]
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. CoRR abs/2303.11328 (2023). arXiv:2303.11328
[32]
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. CoRR abs/2309.03453 (2023). arXiv:2309.03453
[33]
Xiaoxiao Long, Yuan-Chen Guo, Cheng Lin, Yuan Liu, Zhiyang Dou, Lingjie Liu, Yuexin Ma, Song-Hai Zhang, Marc Habermann, Christian Theobalt, and Wenping Wang. 2023. Wonder3D: Single Image to 3D using Cross-Domain Diffusion. CoRR abs/2310.15008 (2023). arXiv:2310.15008
[34]
Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J. Black. 2015. SMPL: a skinned multi-person linear model. ACM Trans. Graph. 34, 6 (2015), 248:1–248:16.
[35]
Luke Melas-Kyriazi, Iro Laina, Christian Rupprecht, and Andrea Vedaldi. 2023. RealFusion 360? Reconstruction of Any Object from a Single Image. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 8446–8455.
[36]
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 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 12663–12673.
[37]
Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. 2022. NeRF: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65, 1 (2022), 99–106.
[38]
MixamoInc. 2009. Mixamo’s online services. https://www.mixamo.com/
[39]
Norman M?ller, Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bul?, Peter Kontschieder, and Matthias Nie?ner. 2023. DiffRF: Rendering-Guided 3D Radiance Field Diffusion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 4328–4338.
[40]
Alex Nichol, Heewoo Jun, Prafulla Dhariwal, Pamela Mishkin, and Mark Chen. 2022. Point-E: A System for Generating 3D Point Clouds from Complex Prompts. CoRR abs/2212.08751 (2022). arXiv:2212.08751
[41]
Georgios Pavlakos, Vasileios Choutas, Nima Ghorbani, Timo Bolkart, Ahmed A. A. Osman, Dimitrios Tzionas, and Michael J. Black. 2019. Expressive Body Capture: 3D Hands, Face, and Body From a Single Image. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019, Long Beach, CA, USA, June 16–20, 2019. Computer Vision Foundation / IEEE, 10975–10985.
[42]
Patrick P?rez, Michel Gangnet, and Andrew Blake. 2003. Poisson image editing. ACM Trans. Graph. 22, 3 (2003), 313–318.
[43]
Pixiv. 2019. VRM tools of three.js. https://github.com/pixiv/three-vrm
[44]
Ben Poole, Ajay Jain, Jonathan T. Barron, and Ben Mildenhall. 2023. DreamFusion: Text-to-3D using 2D Diffusion. In The Eleventh International Conference on Learning Representations, ICLR 2023, Kigali, Rwanda, May 1–5, 2023. OpenReview.net. https://openreview.net/pdf?id=FjNys5c7VyY
[45]
Guocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren, Aliaksandr Siarohin, Bing Li, Hsin-Ying Lee, Ivan Skorokhodov, Peter Wonka, Sergey Tulyakov, and Bernard Ghanem. 2023. Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors. CoRR abs/2306.17843 (2023). arXiv:2306.17843
[46]
Amit Raj, Srinivas Kaza, Ben Poole, Michael Niemeyer, Nataniel Ruiz, Ben Mildenhall, Shiran Zada, Kfir Aberman, Michael Rubinstein, Jonathan T. Barron, Yuanzhen Li, and Varun Jampani. 2023. DreamBooth3D: Subject-Driven Text-to-3D Generation. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1–6, 2023. IEEE, 2349–2359.
[47]
Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman. 2023. DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 22500–22510.
[48]
Shunsuke Saito, Zeng Huang, Ryota Natsume, Shigeo Morishima, Hao Li, and Angjoo Kanazawa. 2019. PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization. In 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 – November 2, 2019. IEEE, 2304–2314.
[49]
Shunsuke Saito, Tomas Simon, Jason M. Saragih, and Hanbyul Joo. 2020. PIFuHD: MultiLevel Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13–19, 2020. Computer Vision Foundation / IEEE, 81–90.
[50]
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. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6–14, 2021, virtual, Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 6087–6101. https://proceedings.neurips.cc/paper/2021/hash/30a237d18c50f563cba4531f1db44acf-Abstract.html
[51]
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. CoRR abs/2310.15110 (2023). arXiv:2310.15110
[52]
Yichun Shi, Peng Wang, Jianglong Ye, Mai Long, Kejie Li, and Xiao Yang. 2023b. MVDream: Multi-view Diffusion for 3D Generation. CoRR abs/2308.16512 (2023). arXiv:2308.16512
[53]
Junshu Tang, Tengfei Wang, Bo Zhang, Ting Zhang, Ran Yi, Lizhuang Ma, and Dong Chen. 2023a. Make-It-3D: High-Fidelity 3D Creation from A Single Image with Diffusion Prior. In IEEE/CVF International Conference on Computer Vision, ICCV 2023, Paris, France, October 1–6, 2023. IEEE, 22762–22772.
[54]
Shitao Tang, Fuyang Zhang, Jiacheng Chen, Peng Wang, and Yasutaka Furukawa. 2023b. MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusion. CoRR abs/2307.01097 (2023). arXiv:2307.01097
[55]
Anh Thai, Stefan Stojanov, Vijay Upadhya, and James M. Rehg. 2021. 3D Reconstruction of Novel Object Shapes from Single Images. In International Conference on 3D Vision, 3DV 2021, London, United Kingdom, December 1–3, 2021. IEEE, 85–95.
[56]
VRoid. 2022. VRoid Hub. https://vroid.com/
[57]
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 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 12619–12629.
[58]
Nanyang Wang, Yinda Zhang, Zhuwen Li, Yanwei Fu, Hang Yu, Wei Liu, Xiangyang Xue, and Yu-Gang Jiang. 2021b. Pixel2Mesh: 3D Mesh Model Generation via Image Guided Deformation. IEEE Trans. Pattern Anal. Mach. Intell. 43, 10 (2021), 3600–3613.
[59]
Peng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt, Taku Komura, and Wenping Wang. 2021a. NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6–14, 2021, virtual, Marc’Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 27171–27183. https://proceedings.neurips.cc/paper/2021/hash/e41e164f7485ec4a28741a2d0ea41c74-Abstract.html
[60]
Peng Wang and Yichun Shi. 2023. ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation. CoRR abs/2312.02201 (2023). arXiv:2312.02201
[61]
Tengfei Wang, Bo Zhang, Ting Zhang, Shuyang Gu, Jianmin Bao, Tadas Baltrusaitis, Jingjing Shen, Dong Chen, Fang Wen, Qifeng Chen, and Baining Guo. 2023c. RODIN: A Generative Model for Sculpting 3D Digital Avatars Using Diffusion. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 4563–4573.
[62]
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. CoRR abs/2305.16213 (2023). arXiv:2305.16213
[63]
Chao Wen, Yinda Zhang, Chenjie Cao, Zhuwen Li, Xiangyang Xue, and Yanwei Fu. 2023. Pixel2Mesh++: 3D Mesh Generation and Refinement From Multi-View Images. IEEE Trans. Pattern Anal. Mach. Intell. 45, 2 (2023), 2166–2180.
[64]
Chao-Yuan Wu, Justin Johnson, Jitendra Malik, Christoph Feichtenhofer, and Georgia Gkioxari. 2023a. Multiview Compressive Coding for 3D Reconstruction. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 9065–9075.
[65]
Tong Wu, Jiarui Zhang, Xiao Fu, Yuxin Wang, Jiawei Ren, Liang Pan, Wayne Wu, Lei Yang, Jiaqi Wang, Chen Qian, Dahua Lin, and Ziwei Liu. 2023b. OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 803–814.
[66]
Yuliang Xiu, Jinlong Yang, Xu Cao, Dimitrios Tzionas, and Michael J. Black. 2023. ECON: Explicit Clothed humans Optimized via Normal integration. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023, Vancouver, BC, Canada, June 17–24, 2023. IEEE, 512–523.
[67]
Yuliang Xiu, Jinlong Yang, Dimitrios Tzionas, and Michael J. Black. 2022. ICON: Implicit Clothed humans Obtained from Normals. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18–24, 2022. IEEE, 13286–13296.
[68]
Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, and Wei Yang. 2023. IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models. CoRR abs/2308.06721 (2023). arXiv:2308.06721
[69]
Paul Yoo, Jiaxian Guo, Yutaka Matsuo, and Shixiang Shane Gu. 2023. DreamSparse: Escaping from Plato’s Cave with 2D Frozen Diffusion Model Given Sparse Views. CoRR abs/2306.03414 (2023). arXiv:2306.03414
[70]
Xiaohui Zeng, Arash Vahdat, Francis Williams, Zan Gojcic, Or Litany, Sanja Fidler, and Karsten Kreis. 2022. LION: Latent Point Diffusion Models for 3D Shape Generation. In Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, NeurIPS 2022, New Orleans, LA, USA, November 28 – December 9, 2022, Sanmi Koyejo, S. Mohamed, A. Agarwal, Danielle Belgrave, K. Cho, and A. Oh (Eds.). http://papers.nips.cc/paper_files/paper/2022/hash/40e56dabe12095a5fc44a6e4c3835948-Abstract-Conference.html
[71]
Biao Zhang, Jiapeng Tang, Matthias Nie?ner, and Peter Wonka. 2023b. 3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models. ACM Trans. Graph. 42, 4 (2023), 92:1–92:16.
[72]
Huichao Zhang, Bowen Chen, Hao Yang, Liao Qu, Xu Wang, Li Chen, Chao Long, Feida Zhu, Kang Du, and Min Zheng. 2023a. AvatarVerse: High-quality & Stable 3D Avatar Creation from Text and Pose. CoRR abs/2308.03610 (2023). arXiv:2308.03610
[73]
Jianfeng Zhang, Xuanmeng Zhang, Huichao Zhang, Jun Hao Liew, Chenxu Zhang, Yi Yang, and Jiashi Feng. 2023c. AvatarStudio: High-fidelity and Animatable 3D Avatar Creation from Text. CoRR abs/2311.17917 (2023). arXiv:2311.17917
[74]
Lvmin Zhang and Maneesh Agrawala. 2023. Adding Conditional Control to Text-to-Image Diffusion Models. CoRR abs/2302.05543 (2023). arXiv:2302.05543
[75]
Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. 2018. The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. In 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18–22, 2018. Computer Vision Foundation / IEEE Computer Society, 586–595.
[76]
Zerong Zheng, Tao Yu, Yebin Liu, and Qionghai Dai. 2022. PaMIR: Parametric Model-Conditioned Implicit Representation for Image-Based Human Reconstruction. IEEE Trans. Pattern Anal. Mach. Intell. 44, 6 (2022), 3170–3184.
