Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts

• URL: http://arxiv.org/abs/2310.11784v2
• Date: Sat, 16 Mar 2024 10:33:40 GMT
• Title: Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts
• Authors: Xinhua Cheng, Tianyu Yang, Jianan Wang, Yu Li, Lei Zhang, Jian Zhang, Li Yuan,
• Abstract summary: We propose a general framework named Progressive3D, which decomposes the entire generation into a series of locally progressive editing steps. We constrain the content change to only occur in regions determined by user-defined region prompts in each editing step. Extensive experiments demonstrate that the proposed Progressive3D framework generates precise 3D content for prompts with complex semantics.
• Score: 38.94299662658179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent text-to-3D generation methods achieve impressive 3D content creation capacity thanks to the advances in image diffusion models and optimizing strategies. However, current methods struggle to generate correct 3D content for a complex prompt in semantics, i.e., a prompt describing multiple interacted objects binding with different attributes. In this work, we propose a general framework named Progressive3D, which decomposes the entire generation into a series of locally progressive editing steps to create precise 3D content for complex prompts, and we constrain the content change to only occur in regions determined by user-defined region prompts in each editing step. Furthermore, we propose an overlapped semantic component suppression technique to encourage the optimization process to focus more on the semantic differences between prompts. Extensive experiments demonstrate that the proposed Progressive3D framework generates precise 3D content for prompts with complex semantics and is general for various text-to-3D methods driven by different 3D representations.

Related papers

• Semantic Score Distillation Sampling for Compositional Text-to-3D Generation [28.88237230872795]
  Generating high-quality 3D assets from textual descriptions remains a pivotal challenge in computer graphics and vision research. We introduce a novel SDS approach, designed to improve the expressiveness and accuracy of compositional text-to-3D generation. Our approach integrates new semantic embeddings that maintain consistency across different rendering views. By leveraging explicit semantic guidance, our method unlocks the compositional capabilities of existing pre-trained diffusion models.
  arXiv  Detail & Related papers  (2024-10-11T17:26:00Z)
• SceneWiz3D: Towards Text-guided 3D Scene Composition [134.71933134180782]
  Existing approaches either leverage large text-to-image models to optimize a 3D representation or train 3D generators on object-centric datasets. We introduce SceneWiz3D, a novel approach to synthesize high-fidelity 3D scenes from text.
  arXiv  Detail & Related papers  (2023-12-13T18:59:30Z)
• TeMO: Towards Text-Driven 3D Stylization for Multi-Object Meshes [67.5351491691866]
  We present a novel framework, dubbed TeMO, to parse multi-object 3D scenes and edit their styles. Our method can synthesize high-quality stylized content and outperform the existing methods over a wide range of multi-object 3D meshes.
  arXiv  Detail & Related papers  (2023-12-07T12:10:05Z)
• ATT3D: Amortized Text-to-3D Object Synthesis [78.96673650638365]
  We amortize optimization over text prompts by training on many prompts simultaneously with a unified model, instead of separately. Our framework - Amortized text-to-3D (ATT3D) - enables knowledge-sharing between prompts to generalize to unseen setups and smooths between text for novel assets and simple animations.
  arXiv  Detail & Related papers  (2023-06-06T17:59:10Z)
• 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)
• Compositional 3D Scene Generation using Locally Conditioned Diffusion [49.5784841881488]
  We introduce textbflocally conditioned diffusion as an approach to compositional scene diffusion. We demonstrate a score distillation sampling--based text-to-3D synthesis pipeline that enables compositional 3D scene generation at a higher fidelity than relevant baselines.
  arXiv  Detail & Related papers  (2023-03-21T22:37:16Z)

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.