CAT3D: Create Anything in 3D with Multi-View Diffusion Models
Ruiqi Gao, Aleksander Holynski, Philipp Henzler, Arthur Brussee, Ricardo Martin-Brualla, Pratul P. Srinivasan, Jonathan T. Barron, Ben Poole
摘要
Advances in 3D reconstruction have enabled high-quality 3D capture, but require a user to collect hundreds to thousands of images to create a 3D scene. We present CAT3D, a method for creating anything in 3D by simulating this real-world capture process with a multi-view diffusion model. Given any number of input images and a set of target novel viewpoints, our model generates highly consistent novel views of a scene. These generated views can be used as input to robust 3D reconstruction techniques to produce 3D representations that can be rendered from any viewpoint in real-time. CAT3D can create entire 3D scenes in as little as one minute, and outperforms existing methods for single image and few-view 3D scene creation. See our project page for results and interactive demos at https://cat3d.github.io .
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 Paper190
- Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion TransformerShuang Wu, Youtian Lin, Yifei Zeng, Feihu Zhang 等NeurIPS 2024 · 被引用 251 次
- WorldMem: Long-term Consistent World Simulation with MemoryZeqi Xiao, Yushi Lan, Yifan Zhou, Wenqi Ouyang 等NeurIPS 2025 · 被引用 165 次
- MVSplat360: Feed-Forward 360 Scene Synthesis from Sparse ViewsYuedong Chen, Chuanxia Zheng, Haofei Xu, Bohan Zhuang 等NeurIPS 2024 · 被引用 126 次
- Cameras as Relative Positional EncodingRuilong Li, Brent Yi, Junchen Liu, Hang Gao 等NeurIPS 2025 · 被引用 113 次
- Meta 3D AssetGen: Text-to-Mesh Generation with High-Quality Geometry, Texture, and PBR MaterialsYawar Siddiqui, Tom Monnier, Filippos Kokkinos, Mahendra Kariya 等NeurIPS 2024 · 被引用 89 次
它引用的顶会 Paper36
- Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 被引用 47,906 次
- High-Resolution Image Synthesis with Latent Diffusion ModelsRobin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser 等CVPR 2022 · 被引用 13,123 次
- Denoising Diffusion Implicit ModelsJiaming Song, Chenlin Meng, Stefano ErmonICLR 2021 · 被引用 11,743 次
- 3D Gaussian Splatting for Real-Time Radiance Field RenderingBernhard Kerbl, Georgios Kopanas, Thomas Leimkühler, George DrettakisSIGGRAPH 2023 · 被引用 5,687 次
- FlashAttention: Fast and Memory-Efficient Exact Attention with IO-AwarenessTri Dao, Daniel Y. Fu, Stefano Ermon, Atri Rudra 等NeurIPS 2022 · 被引用 5,493 次
相关 Paper
- Bolt3D: Generating 3D Scenes in SecondsStanislaw Szymanowicz, Jason Y. Zhang, Pratul P. Srinivasan, Ruiqi Gao 等ICCV 2025 · 被引用 11 次
- Omni-Scene: Omni-Gaussian Representation for Ego-Centric Sparse-View Scene ReconstructionDongxu Wei, Zhiqi Li, Peidong LiuCVPR 2025
- DMV3D: Denoising Multi-view Diffusion Using 3D Large Reconstruction ModelYinghao Xu, Hao Tan, Fujun Luan, Sai Bi 等ICLR 2024 · 被引用 234 次
- Zero-1-to-3: Zero-shot One Image to 3D ObjectRuoshi Liu, Rundi Wu, Basile Van Hoorick, Pavel Tokmakov 等ICCV 2023 · 被引用 1,662 次
- The More You See in 2D, the More You Perceive in 3DXinyang Han, Zelin Gao, Angjoo Kanazawa, Shubham Goel 等CVPR 2024
