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Progressive3D: Progressively Local Editing for Text-to-3D Content Creation with Complex Semantic Prompts (2310.11784v2)

Published 18 Oct 2023 in cs.CV

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.

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

The paper "Progressive3D" by Xinhua Cheng et al. presents a novel framework aimed at enhancing the generation of 3D content from complex semantic text prompts. This work addresses significant challenges in the field of text-to-3D content creation, especially with prompts that involve multiple objects and intricate semantic descriptions.

The proposed framework, Progressive3D, introduces a systematic approach to decompose complex 3D content generation into a series of progressive local editing steps. The authors highlight the limitations of existing methods that struggle to maintain semantic consistency when dealing with complex prompts. Progressive3D constrains content modifications to only specified regions, focusing on semantic differences guided by user-defined prompts in each editing phase.

Key Innovations and Methodological Advancements

  1. Progressive Editing Framework: The paper introduces a method to iteratively build complex 3D scenes by editing a base model through multiple localized editing operations. This progressive approach allows for addressing semantic consistencies incrementally, facilitating a more precise alignment of generated content with intricate prompts.
  2. Region-Specific Constraints: Progressive3D uses user-defined region prompts to ensure changes are limited to desired areas without affecting the rest of the 3D scene. This selective modification is crucial for complex scenes where maintaining certain characteristics of the primary object or environment is essential.
  3. Overlapped Semantic Component Suppression (OSCS): The OSCS technique is a significant contribution, enabling the framework to focus on differences rather than redundancies between source and target prompts. This helps in achieving detailed and specific adjustments, reducing issues like attribute mismatching common in complex prompt scenarios.
  4. Versatility Across 3D Representations: The framework demonstrates compatibility with various 3D neural representations, including those based on NeRF, SDF, and DMTet, proving its efficacy across a wide range of existing methods. This adaptability enhances its applicability in diverse 3D content creation scenarios.

Empirical Evaluation

The experimental evaluation uses CSP-100, a complex semantic prompt set specifically designed to test the proposed methods. Results demonstrate that Progressive3D significantly improves upon existing text-to-3D techniques by achieving better semantic alignment in generated content. Numerical comparisons using metrics like BLIP-VQA and mGPT-CoT exhibit noticeable improvements, with the framework consistently outperforming baseline methods in user preference studies.

Theoretical and Practical Implications

The theoretical implications of this work lie in its novel approach to semantic isolation and enhancement, pushing forward the capabilities of autocreative systems in handling intricate instructions. Practically, Progressive3D presents a robust solution for industries reliant on high-fidelity 3D content, such as entertainment and virtual reality, enabling more intuitive and precise content creation workflows.

Future Directions

Looking ahead, there are intriguing prospects for further developing Progressive3D. Enhanced interaction strategies for defining user prompts, automation in region definition, and integration with real-time processing pipelines could expand its utility and performance. Additionally, exploring this framework's application with next-generation 3D representations could open new avenues for achieving even greater semantic fidelity and operational efficiency.

In conclusion, Progressive3D represents a substantial advancement in the domain of text-guided 3D content creation. By tackling the complexities of semantic detail and offering a structured editing approach, it sets a new benchmark for precision and applicability in the generation of three-dimensional digital artifacts.

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Authors (7)
  1. Xinhua Cheng (21 papers)
  2. Tianyu Yang (67 papers)
  3. Jianan Wang (44 papers)
  4. Yu Li (377 papers)
  5. Lei Zhang (1689 papers)
  6. Jian Zhang (542 papers)
  7. Li Yuan (141 papers)
Citations (31)
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