MaPa: Text-driven Photorealistic Material Painting for 3D Shapes

• URL: http://arxiv.org/abs/2404.17569v2
• Date: Tue, 25 Jun 2024 14:44:52 GMT
• Title: MaPa: Text-driven Photorealistic Material Painting for 3D Shapes
• Authors: Shangzhan Zhang, Sida Peng, Tao Xu, Yuanbo Yang, Tianrun Chen, Nan Xue, Yujun Shen, Hujun Bao, Ruizhen Hu, Xiaowei Zhou,
• Abstract summary: This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs. Our framework supports high-quality rendering and provides substantial flexibility in editing.
• Score: 80.66880375862628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs as the appearance representation, which supports high-quality rendering and provides substantial flexibility in editing. Instead of relying on extensive paired data, i.e., 3D meshes with material graphs and corresponding text descriptions, to train a material graph generative model, we propose to leverage the pre-trained 2D diffusion model as a bridge to connect the text and material graphs. Specifically, our approach decomposes a shape into a set of segments and designs a segment-controlled diffusion model to synthesize 2D images that are aligned with mesh parts. Based on generated images, we initialize parameters of material graphs and fine-tune them through the differentiable rendering module to produce materials in accordance with the textual description. Extensive experiments demonstrate the superior performance of our framework in photorealism, resolution, and editability over existing methods. Project page: https://zju3dv.github.io/MaPa

Related papers

• MTFusion: Reconstructing Any 3D Object from Single Image Using Multi-word Textual Inversion [10.912989885886617]
  We propose MTFusion, which leverages both image data and textual descriptions for high-fidelity 3D reconstruction. Our approach consists of two stages. First, we adopt a novel multi-word textual inversion technique to extract a detailed text description. Then, we use this description and the image to generate a 3D model with FlexiCubes.
  arXiv  Detail & Related papers  (2024-11-19T03:29:18Z)
• MatAtlas: Text-driven Consistent Geometry Texturing and Material Assignment [11.721314027024547]
  MatAtlas is a method for consistent text-guided 3D model. By proposing a multi-step texture refinement process, we significantly improve the quality and consistency.
  arXiv  Detail & Related papers  (2024-04-03T17:57:15Z)
• ViewDiff: 3D-Consistent Image Generation with Text-to-Image Models [65.22994156658918]
  We present a method that learns to generate multi-view images in a single denoising process from real-world data. We design an autoregressive generation that renders more 3D-consistent images at any viewpoint.
  arXiv  Detail & Related papers  (2024-03-04T07:57:05Z)
• GraphDreamer: Compositional 3D Scene Synthesis from Scene Graphs [74.98581417902201]
  We propose a novel framework to generate compositional 3D scenes from scene graphs. By exploiting node and edge information in scene graphs, our method makes better use of the pretrained text-to-image diffusion model. We conduct both qualitative and quantitative experiments to validate the effectiveness of GraphDreamer.
  arXiv  Detail & Related papers  (2023-11-30T18:59:58Z)
• Material Palette: Extraction of Materials from a Single Image [19.410479434979493]
  We propose a method to extract physically-based rendering (PBR) materials from a single real-world image. We map regions of the image to material concepts using a diffusion model, which allows the sampling of texture images resembling each material in the scene. Second, we benefit from a separate network to decompose the generated textures into Spatially Varying BRDFs.
  arXiv  Detail & Related papers  (2023-11-28T18:59:58Z)
• TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models [77.85129451435704]
  We present a new method to synthesize textures for 3D, using large-scale-guided image diffusion models. Specifically, we leverage latent diffusion models, apply the set denoising model and aggregate denoising text map.
  arXiv  Detail & Related papers  (2023-10-20T19:15:29Z)
• Guide3D: Create 3D Avatars from Text and Image Guidance [55.71306021041785]
  Guide3D is a text-and-image-guided generative model for 3D avatar generation based on diffusion models. Our framework produces topologically and structurally correct geometry and high-resolution textures.
  arXiv  Detail & Related papers  (2023-08-18T17:55:47Z)
• TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo Supervision [114.56048848216254]
  We present a novel framework, TAPS3D, to train a text-guided 3D shape generator with pseudo captions. Based on rendered 2D images, we retrieve relevant words from the CLIP vocabulary and construct pseudo captions using templates. Our constructed captions provide high-level semantic supervision for generated 3D shapes.
  arXiv  Detail & Related papers  (2023-03-23T13:53:16Z)
• Text2Room: Extracting Textured 3D Meshes from 2D Text-to-Image Models [21.622420436349245]
  We present Text2Room, a method for generating room-scale textured 3D meshes from a given text prompt as input. We leverage pre-trained 2D text-to-image models to synthesize a sequence of images from different poses. In order to lift these outputs into a consistent 3D scene representation, we combine monocular depth estimation with a text-conditioned inpainting model.
  arXiv  Detail & Related papers  (2023-03-21T16:21:02Z)
• Photo-to-Shape Material Transfer for Diverse Structures [15.816608726698986]
  We introduce a method for assigning photorealistic relightable materials to 3D shapes in an automatic manner. Our method combines an image translation neural network with a material assignment neural network. We demonstrate that our method allows us to assign materials to shapes so that their appearances better resemble the input exemplars.
  arXiv  Detail & Related papers  (2022-05-09T03:37:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.