Beyond Voxel 3D Editing: Learning from 3D Masks and Self-Constructed Data
Summary: arXiv:2604.13688v1 Announce Type: cross
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.
Introduction
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. This enables efficient injection of textual semantics without the need for expensive full-model retraining.
Challenges in Current 3D Editing Techniques
Recent advancements in 3D editing have showcased impressive capabilities; however, several challenges persist:
- Multi-View Editing Limitations: Existing methods often suffer from losses when projecting back to 3D, leading to inconsistencies in the final output.
- Voxel-Based Editing Constraints: Voxel-based approaches are limited in the regions that can be modified and the scale of modifications, impacting the flexibility required for comprehensive editing.
- Lack of Large Datasets: The absence of sufficiently large editing datasets hampers the training and evaluation of 3D editing models, leading to suboptimal performance.
The BVE Framework
The Beyond Voxel 3D Editing framework introduces a novel approach to 3D editing, focusing on semantic preservation and localized modifications. Key features of the BVE framework include:
- Self-Constructed Dataset: Our dataset is specifically designed for 3D editing tasks, addressing the gaps in available training data.
- Lightweight Trainable Modules: The architecture allows for the integration of textual semantics without extensive retraining, enhancing usability and efficiency.
- Annotation-Free 3D Masking Strategy: This innovative strategy preserves local invariance, ensuring that unchanged regions maintain their integrity during the editing process.
Performance and Results
Extensive experiments conducted on the BVE framework demonstrate its superior performance in generating high-quality, text-aligned 3D assets. The results indicate that BVE faithfully retains the visual characteristics of the original input while allowing for effective modifications. This advancement marks a significant step forward in the realm of 3D editing, showcasing the potential for more intuitive and flexible editing processes.
Conclusion
The Beyond Voxel 3D Editing framework represents a substantial advancement in 3D editing technologies, addressing previous limitations and paving the way for future developments. By leveraging self-constructed datasets and innovative methodologies, BVE not only enhances the editing process but also contributes to the broader field of generative models in 3D spaces.