TextDeformer: Geometry Manipulation using Text Guidance
William Gao, Noam Aigerman, Thibault Groueix, Vova Kim, Rana Hanocka
摘要
Fig. 1. TextDeformer deforms a source shape into various text-specified targets. The mesh colors visualize the smoothness of the mappings.
We present a technique for automatically producing a deformation of an input triangle mesh, guided solely by a text prompt. Our framework is capable of deformations that produce both large, low-frequency shape changes, and small high-frequency details. Our framework relies on differentiable rendering to connect geometry to powerful pre-trained image encoders, such as CLIP and DINO. Notably, updating mesh geometry by taking gradient steps through differentiable rendering is notoriously challenging, commonly resulting in deformed meshes with significant artifacts. These difficulties are amplified by noisy and inconsistent gradients from CLIP. To overcome this limitation, we opt to represent our mesh deformation through Jacobians, which updates deformations in a global, smooth manner (rather than locallysub-optimal steps). Our key observation is that Jacobians are a representation that favors smoother, large deformations, leading to a global relation between vertices and pixels, and avoiding localized noisy gradients. Additionally, to ensure the resulting shape is coherent from all 3D viewpoints, we encourage the deep features computed on the 2D encoding of the rendering to be consistent for a given vertex from all viewpoints. We demonstrate that our method is capable of smoothly-deforming a wide variety of source mesh and target text prompts, achieving both large modifications to, e.g., body proportions of animals, as well as adding fine semantic details, such as shoe laces on an army boot and fine details of a face.
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 Paper46
- Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion PriorsGuocheng Qian, Jinjie Mai, Abdullah Hamdi, Jian Ren 等ICLR 2024 · 被引用 444 次
- GaussianEditor: Editing 3D Gaussians Delicately with Text InstructionsJunjie Wang, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie 等CVPR 2024 · 被引用 65 次
- HumanGaussian: Text-Driven 3D Human Generation with Gaussian SplattingXian Liu, Xiaohang Zhan, Jiaxiang Tang, Ying Shan 等CVPR 2024 · 被引用 42 次
- Style2Fab: Functionality-Aware Segmentation for Fabricating Personalized 3D Models with Generative AIFaraz Faruqi, Ahmed Katary, Tarik Hasic, Amira Abdel-Rahman 等UIST 2023 · 被引用 39 次
- Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-DistillationSherwin Bahmani, Tianchang Shen, Jiawei Ren, Jiahui Huang 等ICLR 2026 · 被引用 33 次
它引用的顶会 Paper23
- 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
- Photorealistic Text-to-Image Diffusion Models with Deep Language UnderstandingChitwan Saharia, William Chan, Saurabh Saxena, Lala Li 等NeurIPS 2022 · 被引用 8,965 次
- GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion ModelsAlexander Quinn Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam 等ICML 2022 · 被引用 4,691 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
相关 Paper
- Text2Mesh: Text-Driven Neural Stylization for MeshesOscar Michel, Roi Bar-On, Richard Liu, Sagie Benaim 等CVPR 2022
- 3D Highlighter: Localizing Regions on 3D Shapes via Text DescriptionsDale Decatur, Itai Lang, Rana HanockaCVPR 2023
- TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting DecompositionYongwei Chen, Rui Chen, Jiabao Lei, Yabin Zhang 等NeurIPS 2022 · 被引用 112 次
- CLIPVG: Text-Guided Image Manipulation Using Differentiable Vector GraphicsYiren Song, Xuning Shao, Kang Chen, Weidong Zhang 等AAAI 2023 · 被引用 50 次
- Text2Tex: Text-driven Texture Synthesis via Diffusion ModelsDave Zhenyu Chen, Yawar Siddiqui, Hsin-Ying Lee, Sergey Tulyakov 等ICCV 2023 · 被引用 262 次
