Sketch2Model: View-Aware 3D Modeling from Single Free-Hand Sketches

By Song-Hai Zhang, Yuanchen Guo, Qing-Wen Gu. 2021

114 citations recorded by the source corpus.

We investigate the problem of generating 3D meshes from single free-hand sketches, aiming at fast 3D modeling for novice users. It can be regarded as a single-view reconstruction problem, but with unique challenges, brought by the variation and conciseness of sketches. Ambiguities in poorly-drawn sketches could make it hard to determine how the sketched object is posed. In this paper, we address the importance of viewpoint specification for overcoming such ambiguities, and propose a novel view-aware generation approach. By explicitly conditioning the generation process on a given viewpoint, our method can generate plausible shapes automatically with predicted viewpoints, or with specified viewpoints to help users better express their intentions. Extensive evaluations on various datasets demonstrate the effectiveness of our view-aware design in solving sketch ambiguities and improving reconstruction quality.

Subfields: Computational Mechanics, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (2022, background): “…41, 46, 34, 53, 6, 2], sketch has also been employed in a variety of vision understanding tasks, including segmentation [22], video synthesis [28], representation learning [60, 3], object localisation [55], image-inpainting [62], 3D shape retrieval [31], 3D shape modelling [71], among others [64].”
Deep Learning for Free-Hand Sketch: A Survey (2020, methodology): “(i) Sketch can be formatted in visual domain as picture, so that most of the common data augmentations designed for natural photos ( e . g ., ImageNet [84] photos) can be applied to sketch, e . g ., horizontal reﬂection/mirroring, rotation, horizontal shift, vertical shift, central zoom.”
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation (2025, unknown): “Previous methods [1, 121, 118, 32] suffer from the lack of the generative foundation model.”