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Related Experiment Video

Updated: Jun 18, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

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Published on: April 21, 2023

SketchBodyNet++: Sketch-Based 3D Human Mesh Reconstruction Via Hybrid Parametric Networks.

Fei Wang, Jiaxin Zhang, Xuyang Liu

IEEE Transactions on Visualization and Computer Graphics

|June 16, 2026

Summary

This summary is machine-generated.

This study introduces a novel framework for reconstructing 3D human meshes from sketches, overcoming limitations of existing methods. The approach effectively generates accurate 3D human models from abstract sketch inputs.

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Area of Science:

Computer Vision
3D Reconstruction
Machine Learning

Background:

Sketches are valuable for 3D human mesh generation but existing methods struggle with their abstract nature.
A lack of large-scale sketch-to-mesh datasets hinders development and evaluation.

Purpose of the Study:

To develop a robust framework for 3D human mesh reconstruction from sketches.
To introduce a new large-scale dataset for sketch-based 3D human mesh research.

Main Methods:

A hybrid framework combining a Local Image Encoder (LIE) and Global Point Encoder (GPE) for coarse-to-fine mesh fitting.
Utilizing a graph-based refiner (GRefiner) to integrate local pose and global shape information.
Creation of the Sketch3DS dataset with ~10,000 sketch-mesh pairs.

Main Results:

The proposed framework accurately reconstructs 3D human meshes from sketches.
Achieved superior performance compared to existing methods on the Sketch3DS dataset.
Demonstrated effective alignment between input sketches and generated 3D human meshes.

Conclusions:

The novel hybrid framework successfully addresses the challenges of 3D human mesh reconstruction from sketches.
The Sketch3DS dataset provides a valuable resource for advancing research in this domain.
The method offers a significant improvement in generating accurate and well-fitted 3D human meshes from sketches.