Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images

Abstract: Recent advances in 3D AIGC have shown promise in directly creating 3D objects from text and images, offering significant cost savings in animation and product design. However, detailed edit and customization of 3D assets remains a long-standing challenge. Specifically, 3D Generation methods lack the ability to follow finely detailed instructions as precisely as their 2D image creation counterparts. Imagine you can get a toy through 3D AIGC but with undesired accessories and dressing. To tackle this challenge, we propose a novel pipeline called Tailor3D, which swiftly creates customized 3D assets from editable dual-side images. We aim to emulate a tailor's ability to locally change objects or perform overall style transfer. Unlike creating 3D assets from multiple views, using dual-side images eliminates conflicts on overlapping areas that occur when editing individual views. Specifically, it begins by editing the front view, then generates the back view of the object through multi-view diffusion. Afterward, it proceeds to edit the back views. Finally, a Dual-sided LRM is proposed to seamlessly stitch together the front and back 3D features, akin to a tailor sewing together the front and back of a garment. The Dual-sided LRM rectifies imperfect consistencies between the front and back views, enhancing editing capabilities and reducing memory burdens while seamlessly integrating them into a unified 3D representation with the LoRA Triplane Transformer. Experimental results demonstrate Tailor3D's effectiveness across various 3D generation and editing tasks, including 3D generative fill and style transfer. It provides a user-friendly, efficient solution for editing 3D assets, with each editing step taking only seconds to complete.

• The paper introduces Tailor3D, an AI pipeline that enhances 3D asset generation and editing by efficiently utilizing information from dual-sided images via a Dual-sided LRM.
• Tailor3D employs an editing pipeline that generates a back view from a front image and fuses both using the Dual-sided LRM to ensure geometric and texture consistency.
• The system achieves sub-second editing speed and demonstrates superior quality in generative fill and style transfer, with practical applications in animation and game design.

Tailor3D: Enhancing Customized 3D Asset Creation and Editing

The research paper titled "Tailor3D: Customized 3D Assets Editing and Generation with Dual-Side Images" introduces Tailor3D, an advanced pipeline for the rapid generation and customization of 3D assets. Tailor3D leverages dual-sided images to address challenges in detailed 3D asset customization, utilizing modern advancements in AI-generated content (AIGC) technologies.

Overview of Tailor3D

The core innovation of Tailor3D lies in its utilization of the Dual-sided Latent Reconstruction Model (LRM), which enhances 3D generation and editing capabilities by incorporating information from both the front and back images. Unlike traditional multi-view generation methods, Tailor3D focuses on dual-view reconstruction, simplifying operations and improving efficiency.

Methodology Highlights

1. Editing Pipeline: Tailor3D allows for image-based editing — starting with the front view, generating the back view via multi-view diffusion, and integrating both through dual-sided feature fusion. This approach circumvents some of the inherent inconsistencies and memory burdens associated with single-view processing.
2. Dual-sided LRM: The proposed Dual-sided LRM performs feature alignment and stitching akin to a tailor sewing different garment parts. This model enhances the quality of 3D reconstructions by addressing view inconsistencies between the front and back images, ultimately yielding more cohesive 3D representations.
3. LoRA Triplane Transformer: Incorporating Low-Rank Adaptation (LoRA), the system efficiently fine-tunes the model on an extensive set of triplane features while keeping memory usage minimal.

Experimental Results

The study demonstrates Tailor3D’s efficacy in various 3D editing tasks such as generative fill, style transfer, and pattern fusion. Tailor3D not only refines the 3D aesthetics but also provides a sub-second level efficiency in editing operations.

1. Quantitative Metrics: Tailor3D shows competitive performance against state-of-the-art methods in terms of LPIPS, SSIM, and PSNR — metrics that evaluate perceptual quality, structural similarity, and peak signal-to-noise ratio.
2. Qualitative Assessments: In visual comparisons, Tailor3D consistently delivers superior geometry and texture coherence, establishing itself as a robust solution for rapid 3D customization.

Implications and Future Directions

The practical implications of Tailor3D extend to industries involved in animation, game design, and product visualization, providing a cost-effective and highly editable framework for 3D content creation.

Theoretically, Tailor3D showcases the potential for simplifying complex multi-view reconstruction by effectively utilizing dual-view perspectives. The concept of dual-sided editing provides an intriguing avenue for future research — specifically exploring its applications in varying contexts of 3D operations or integrating it with other facade-based information systems.

Future research could focus on overcoming limitations related to object thickness and resolution in geometric modifications, as well as expanding Tailor3D's framework to accommodate environments with even higher complexities and variations.

In summary, Tailor3D represents a significant advancement in the customization and editing of 3D assets, bridging traditional 2D AIGC techniques and modern 3D generation needs with impressive speed and flexibility. Its development and continued refinement hold promise for expanding the boundaries of virtual asset creation and manipulation.