Magic3DSketch: Create Colorful 3D Models From Sketch-Based 3D Modeling Guided by Text and Language-Image Pre-Training
Ying Zang, Yidong Han, Chaotao Ding, Jianqi Zhang, Tianrun Chen

TL;DR
Magic3DSketch enables novice users to create detailed, colorful 3D models from sketches guided by text, leveraging pre-trained language-image models for higher control, realism, and user satisfaction.
Contribution
It introduces a novel sketch-to-3D modeling method guided by text and pre-trained models, adding color and improving controllability over existing approaches.
Findings
Achieves state-of-the-art performance on synthetic and real datasets.
Produces more detailed and realistic 3D shapes with text guidance.
Users report higher satisfaction and controllability.
Abstract
The requirement for 3D content is growing as AR/VR application emerges. At the same time, 3D modelling is only available for skillful experts, because traditional methods like Computer-Aided Design (CAD) are often too labor-intensive and skill-demanding, making it challenging for novice users. Our proposed method, Magic3DSketch, employs a novel technique that encodes sketches to predict a 3D mesh, guided by text descriptions and leveraging external prior knowledge obtained through text and language-image pre-training. The integration of language-image pre-trained neural networks complements the sparse and ambiguous nature of single-view sketch inputs. Our method is also more useful and offers higher degree of controllability compared to existing text-to-3D approaches, according to our user study. Moreover, Magic3DSketch achieves state-of-the-art performance in both synthetic and real…
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Taxonomy
TopicsHuman Motion and Animation · Augmented Reality Applications
