Generative Escher Meshes
Noam Aigerman, Thibault Groueix
摘要
This paper proposes a fully-automatic, text-guided generative method for producing perfectly-repeating, periodic, tile-able 2D imagery, such as the one seen on floors, mosaics, ceramics, and the work of M.C. Escher. In contrast to square texture images that are seamless when tiled, our method generates non-square tilings which comprise solely of repeating copies of the same object. It achieves this by optimizing both geometry and texture of a 2D mesh, yielding a non-square tile in the shape and appearance of the desired object, with close to no additional background details, that can tile the plane without gaps nor overlaps. We enable optimization of the tile’s shape by an unconstrained, differentiable parameterization of the space of all valid tileable meshes for given boundary conditions stemming from a symmetry group. Namely, we construct a differentiable family of linear systems derived from a 2D mesh-mapping technique - Orbifold Tutte Embedding - by considering the mesh’s Laplacian matrix as differentiable parameters. We prove that the solution space of these linear systems is exactly all possible valid tiling configurations, thereby providing an end-to-end differentiable representation for the entire space of valid tiles. We render the textured mesh via a differentiable renderer, and leverage a pre-trained image diffusion model to induce a loss on the resulting image, updating the mesh’s parameters so as to make its appearance match the text prompt. We show our method is able to produce plausible, appealing results, with non-trivial tiles, for a variety of different periodic tiling patterns.
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 Paper3
- TexTile: A Differentiable Metric for Texture TileabilityCarlos Rodríguez-Pardo, Dan Casas, Elena Garces, Jorge Lopez-MorenoCVPR 2024 · 被引用 11 次
- TutteNet: Injective 3D Deformations by Composition of 2D Mesh DeformationsBo Sun, Thibault Groueix, Chen Song, Qixing Huang 等CVPR 2024 · 被引用 1 次
- Escher Tile Deformation via Closed-Form SolutionCrane He Chen, Vladimir G. KimSIGGRAPH 2025
它引用的顶会 Paper18
- 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
- Denoising Diffusion Probabilistic ModelsJonathan Ho, Ajay Jain, Pieter AbbeelNeurIPS 2020 · 被引用 35,902 次
- Diffusion Models Beat GANs on Image SynthesisPrafulla Dhariwal, Alexander Quinn NicholNeurIPS 2021 · 被引用 13,211 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
相关 Paper
- Controllable Texture Tiling via Diffusion Transformers with Transformed Rotary EmbeddingsJunrong Huang, Zhiyuan Zhang, Rui Tang, Hongbo Fu 等SIGGRAPH 2026
- TEXTure: Text-Guided Texturing of 3D ShapesElad Richardson, Gal Metzer, Yuval Alaluf, Raja Giryes 等SIGGRAPH 2023 · 被引用 196 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
- MaPa: Text-driven Photorealistic Material Painting for 3D ShapesShangzhan Zhang, Sida Peng, Tao Xu, Yuanbo Yang 等SIGGRAPH 2024 · 被引用 15 次
- DITTO: Diffusion Inference-Time T-Optimization for Music GenerationZachary Novack, Julian J. McAuley, Taylor Berg-Kirkpatrick, Nicholas J. BryanICML 2024 · 被引用 81 次
