Beyond Voxel 3D Editing: Learning from 3D Masks and Self-Constructed Data
Proposed BVE framework achieves high-quality 3D editing using 3D masks and self-constructed data, significantly improving editing performance.
Key Findings
Methodology
The BVE framework enhances image-to-3D generative architecture with self-constructed large-scale datasets and lightweight trainable modules. It introduces an annotation-free 3D masking strategy to preserve local invariance, allowing efficient injection of textual semantics without full-model retraining.
Key Results
- BVE excels in generating high-quality, text-aligned 3D assets, retaining the visual characteristics of the original input.
- On the Edit-3DVerse dataset, BVE achieved a Chamfer distance of 0.013, significantly outperforming other methods.
- User studies indicate BVE's superiority in text alignment and overall 3D quality over TRELLIS and Hunyuan.
Significance
The BVE framework is significant in the 3D editing field, addressing limitations of existing methods in multi-view consistency and voxel editing. By using self-constructed datasets and innovative 3D masking strategies, BVE offers a more efficient 3D editing solution, enhancing flexibility and accuracy in 3D content generation.
Technical Contribution
BVE achieves efficient 3D editing without full-model retraining through lightweight modules and 3D masking strategies. Compared to existing methods, BVE excels in maintaining local invariance and semantic consistency, offering new engineering possibilities.
Novelty
BVE is the first to combine self-constructed datasets and annotation-free 3D masking strategies for efficient text-driven 3D editing. Compared to methods like VoxHammer, BVE performs better in local editing precision and global structure preservation.
Limitations
- BVE may struggle with complex geometric structures, especially in high-detail scenes.
- The diversity of the self-constructed dataset may limit the model's generalization capabilities.
Future Work
Future work could explore more complex 3D scene editing and broader dataset applications to further enhance BVE's editing capabilities and generalization performance.
AI Executive Summary
3D editing technology is becoming increasingly important, especially for applications requiring local or global modifications to 3D assets. However, existing methods face significant limitations in multi-view consistency and voxel editing. To address these issues, this paper proposes the Beyond Voxel 3D Editing (BVE) framework, which enhances image-to-3D generative architecture with self-constructed large-scale datasets and lightweight trainable modules. BVE introduces an annotation-free 3D masking strategy to preserve local invariance, ensuring consistency in unchanged regions during editing.
In experiments, BVE excels in generating high-quality, text-aligned 3D assets, retaining the visual characteristics of the original input. Specifically, BVE achieved a Chamfer distance of 0.013 on the Edit-3DVerse dataset, significantly outperforming other methods. Additionally, user studies indicate BVE's superiority in text alignment and overall 3D quality over TRELLIS and Hunyuan.
The introduction of the BVE framework brings new possibilities to the 3D editing field, addressing limitations of existing methods in multi-view consistency and voxel editing. By using self-constructed datasets and innovative 3D masking strategies, BVE offers a more efficient 3D editing solution, enhancing flexibility and accuracy in 3D content generation. Future work could explore more complex 3D scene editing and broader dataset applications to further enhance BVE's editing capabilities and generalization performance.
Deep Analysis
Background
3D editing technology is becoming increasingly important in fields such as 3D printing, gaming, virtual reality, and augmented reality. Recently, generative artificial intelligence has played a crucial role in 3D content creation, driving the generation of high-resolution 3D assets. However, existing methods face significant limitations in multi-view consistency and voxel editing, making it challenging to meet user demands for high-quality 3D assets.
Core Problem
Existing 3D editing methods face significant limitations in multi-view consistency and voxel editing. Multi-view editing methods incur losses when projecting back to 3D, while voxel-based editing is constrained in the regions that can be modified and the scale of modifications. Additionally, the lack of sufficiently large editing datasets for training and evaluation remains a challenge.
Innovation
The BVE framework enhances image-to-3D generative architecture with self-constructed large-scale datasets and lightweight trainable modules. It introduces an annotation-free 3D masking strategy to preserve local invariance, ensuring consistency in unchanged regions during editing. Compared to existing methods, BVE performs better in local editing precision and global structure preservation.
Methodology
- �� Self-constructed large-scale datasets enhance model training and evaluation.
- �� Lightweight trainable modules enhance image-to-3D generative architecture.
- �� Annotation-free 3D masking strategy preserves local invariance.
- �� Efficient injection of textual semantics without full-model retraining.
Experiments
Experiments were conducted on the Edit-3DVerse dataset to validate BVE's performance. Chamfer distance, SSIM, and LPIPS metrics were used to evaluate 3D consistency and generation quality. Comparisons with methods like Vox-E, Tailor3D, and TRELLIS demonstrate BVE's superiority.
Results
BVE excels in generating high-quality, text-aligned 3D assets, retaining the visual characteristics of the original input. On the Edit-3DVerse dataset, BVE achieved a Chamfer distance of 0.013, significantly outperforming other methods. User studies indicate BVE's superiority in text alignment and overall 3D quality over TRELLIS and Hunyuan.
Applications
BVE can be applied in scenarios requiring high-quality 3D editing, such as game development, virtual reality content creation, and 3D printing. Its efficient editing capabilities and flexibility make it widely applicable in these fields.
Limitations & Outlook
BVE may struggle with complex geometric structures, especially in high-detail scenes. The diversity of the self-constructed dataset may limit the model's generalization capabilities. Future work could explore more complex 3D scene editing and broader dataset applications to further enhance BVE's editing capabilities and generalization performance.
Plain Language Accessible to non-experts
Imagine a factory where BVE acts like an intelligent production line. Traditional production lines require manual adjustments for each product, but BVE automates the process, quickly adjusting certain parts of the product based on input instructions without affecting others. It's like having a smart worker on the production line who understands instructions and makes precise adjustments without redesigning the entire product.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a game where you can change the characters without restarting. BVE is like a super tool that lets you quickly change parts of a 3D model based on instructions without affecting other parts. It's as simple as changing outfits in a game!
Glossary
3D Editing
Refers to the ability to apply local or global modifications to 3D assets.
Used in the paper to describe the core functionality of the BVE framework.
Voxel
A small cubic unit in 3D space used to represent 3D models.
Describes the limitations of existing methods in BVE.
Semantic Consistency
Refers to maintaining the semantic and structural integrity of unchanged regions during editing.
Achieved in BVE through the 3D masking strategy.
Self-Constructed Dataset
A large-scale dataset created by researchers for model training and evaluation.
Enhances model performance in BVE.
Lightweight Module
A small trainable module that enhances model functionality without full-model retraining.
Enables efficient textual semantic injection in BVE.
Open Questions Unanswered questions from this research
- 1 How to maintain editing precision and consistency in high-detail scenes?
- 2 How does the diversity of the self-constructed dataset affect the model's generalization capabilities?
Applications
Immediate Applications
Game Development
BVE can be used to quickly modify 3D models in games, improving development efficiency.
Long-term Vision
Virtual Reality Content Creation
BVE can be used to create more complex and interactive virtual reality content, driving industry development.
Abstract
3D editing refers to the ability to apply local or global modifications to 3D assets. Effective 3D editing requires maintaining semantic consistency by performing localized changes according to prompts, while also preserving local invariance so that unchanged regions remain consistent with the original. However, existing approaches have significant limitations: multi-view editing methods incur losses when projecting back to 3D, while voxel-based editing is constrained in both the regions that can be modified and the scale of modifications. Moreover, the lack of sufficiently large editing datasets for training and evaluation remains a challenge. To address these challenges, we propose a Beyond Voxel 3D Editing (BVE) framework with a self-constructed large-scale dataset specifically tailored for 3D editing. Building upon this dataset, our model enhances a foundational image-to-3D generative architecture with lightweight, trainable modules, enabling efficient injection of textual semantics without the need for expensive full-model retraining. Furthermore, we introduce an annotation-free 3D masking strategy to preserve local invariance, maintaining the integrity of unchanged regions during editing. Extensive experiments demonstrate that BVE achieves superior performance in generating high-quality, text-aligned 3D assets, while faithfully retaining the visual characteristics of the original input.