HumanGen: Generating Human Radiance Fields with Explicit Priors
Suyi Jiang, Haoran Jiang, Ziyu Wang, Haimin Luo, Wenzheng Chen, Lan Xu
摘要
Recent years have witnessed the tremendous progress of 3D GANs for generating view-consistent radiance fields with photo-realism. Yet, high-quality generation of human radiance fields remains challenging, partially due to the limited human-related priors adopted in existing methods. We present HumanGen, a novel 3D human generation scheme with detailed geometry and 360 • realistic free-view rendering. It explicitly marries the 3D human generation with various priors from the 2D generator and 3D reconstructor of humans through the design of "anchor image". We introduce a hybrid feature representation using the anchor image to bridge the latent space of HumanGen with the existing 2D generator. We then adopt a pronged design to disentangle the generation of geometry and appearance. With the aid of the anchor image, we adapt a 3D reconstructor for fine-grained details synthesis and propose a two-stage blending scheme to boost appearance generation. Extensive experiments demonstrate our effectiveness for state-of-the-art 3D human generation regarding geometry details, texture quality, and free-view performance. Notably, HumanGen can also incorporate various off-the-shelf 2D latent editing methods, seamlessly lifting them into 3D.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper12
- PrimDiffusion: Volumetric Primitives Diffusion for 3D Human GenerationZhaoxi Chen, Fangzhou Hong, Haiyi Mei, Guangcong Wang 等NeurIPS 2023 · 被引用 44 次
- MVHumanNet: A Large-Scale Dataset of Multi-View Daily Dressing Human CapturesZhangyang Xiong, Chenghong Li, Kenkun Liu, Hongjie Liao 等CVPR 2024 · 被引用 16 次
- HAVE-FUN: Human Avatar Reconstruction from Few-Shot Unconstrained ImagesXihe Yang, Xingyu Chen, Daiheng Gao, Shaohui Wang 等CVPR 2024 · 被引用 11 次
- HPC: Hierarchical Progressive Coding Framework for Volumetric VideoZihan Zheng, Houqiang Zhong, Qiang Hu, Xiaoyun Zhang 等ACM MM 2024 · 被引用 9 次
- En3D: An Enhanced Generative Model for Sculpting 3D Humans from 2D Synthetic DataYifang Men, Biwen Lei, Yuan Yao, Miaomiao Cui 等CVPR 2024 · 被引用 7 次
它引用的顶会 Paper52
- Instant neural graphics primitives with a multiresolution hash encodingThomas Müller, Alex Evans, Christoph Schied, Alexander KellerSIGGRAPH 2022 · 被引用 4,089 次
- NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view ReconstructionPeng Wang, Lingjie Liu, Yuan Liu, Christian Theobalt 等NeurIPS 2021 · 被引用 2,500 次
- Alias-Free Generative Adversarial NetworksTero Karras, Miika Aittala, Samuli Laine, Erik Härkönen 等NeurIPS 2021 · 被引用 2,126 次
- StyleCLIP: Text-Driven Manipulation of StyleGAN ImageryOr Patashnik, Zongze Wu, Eli Shechtman, Daniel Cohen-Or 等ICCV 2021 · 被引用 1,437 次
- Volume Rendering of Neural Implicit SurfacesLior Yariv, Jiatao Gu, Yoni Kasten, Yaron LipmanNeurIPS 2021 · 被引用 1,421 次
相关 Paper
- 3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose MappingZhuoqian Yang, Shikai Li, Wayne Wu, Bo DaiICCV 2023 · 被引用 19 次
- DoubleField: Bridging the Neural Surface and Radiance Fields for High-fidelity Human Reconstruction and RenderingRuizhi Shao, Hongwen Zhang, He Zhang, Mingjia Chen 等CVPR 2022 · 被引用 72 次
- 3DHumanEdit: Multi-modal Body Part-aware Conditioning Information Integration for 3D Human ManipulationFeifan Xu, Tianyi Chen, Fan Yang, Yunfei Zhang 等AAAI 2025
- Generative Neural Articulated Radiance FieldsAlexander W. Bergman, Petr Kellnhofer, Wang Yifan, Eric R. Chan 等NeurIPS 2022 · 被引用 144 次
- HoloGAN: Unsupervised Learning of 3D Representations From Natural ImagesThu Nguyen-Phuoc, Chuan Li, Lucas Theis, Christian Richardt 等ICCV 2019 · 被引用 98 次
