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

Xinhua Cheng, Tianyu Yang+5 more|arXiv (Cornell University)|Oct 18, 2023

Computer Graphics and Visualization Techniques4 citations

TL;DR

Progressive3D proposes a general framework for precise text-to-3D generation under complex semantic prompts by decomposing the generation into localized, progressive editing steps. It uses region-guided constraints and Overlapped Semantic Component Suppression (OSCS) to improve semantic alignment, achieving state-of-the-art performance on complex prompts across multiple 3D representations, with 83.2% human preference over baseline methods.

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.

Motivation & Objective

• To address the challenge of generating accurate 3D content when text prompts describe multiple objects with complex, interrelated attributes.
• To overcome limitations in existing text-to-3D methods that suffer from object missing, attribute mismatching, and quality degradation under complex semantics.
• To develop a generalizable framework compatible with diverse 3D neural representations and optimization strategies.
• To improve semantic consistency between generated 3D content and complex prompts through progressive, localized editing.

Proposed method

• Decomposes complex text-to-3D generation into a sequence of local editing steps guided by user-defined region prompts.
• Applies a consistency constraint to preserve content outside the edited region and an initialization constraint to promote geometry generation from empty space.
• Introduces Overlapped Semantic Component Suppression (OSCS) to isolate and emphasize semantic differences between source and target prompts.
• Uses a latent diffusion-based optimization process that focuses on editing only the specified region while minimizing interference elsewhere.
• Leverages CLIP-based embeddings to compute semantic differences and guide the optimization process via attention masking.
• Supports various 3D representations including NeRF, SDF, and DMTet, enabling broad compatibility with existing text-to-3D models.

Experimental results

Research questions

• RQ1 Can progressive, localized editing improve semantic alignment in text-to-3D generation for complex prompts?
• RQ2 How effective is Overlapped Semantic Component Suppression (OSCS) in reducing distraction from shared attributes in complex prompts?
• RQ3 Does the proposed framework generalize across different 3D neural representations and optimization pipelines?
• RQ4 To what extent does progressive editing outperform direct fine-tuning in maintaining structural and attribute consistency?
• RQ5 How do the individual components (consistency constraint, initialization constraint, OSCS) contribute to overall performance?

Key findings

• Progressive3D achieves a 0.474 B-VQA score and 0.609 mGPT-CoT score on the CSP-100 benchmark, significantly outperforming the baseline DreamTime (0.227 and 0.522).
• In human preference studies, 83.2% of users preferred Progressive3D-generated results over DreamTime, indicating strong perceptual alignment with complex prompts.
• The ablation study confirms that combining the initialization constraint and OSCS yields the highest performance, with a 0.474 B-VQA score when all components are used.
• OSCS with increasing suppression weight improves focus on semantic differences, reducing object missing and attribute mismatching.
• The framework successfully mitigates issues like object missing and attribute mismatching in complex prompts, as validated through qualitative and quantitative analysis.
• The method generalizes across multiple 3D representations, including NeRF, SDF, and DMTet, demonstrating broad applicability.