Look Both Ways Before You Cross: Lifting Cross Fields From 2D Visual Priors

2026-05-25

AI Summary

This work addresses the challenge of extracting semantically aligned cross fields from a single 2D image and robustly lifting them onto 3D mesh surfaces while preserving consistency in occluded regions. To this end, the authors propose CrossLift, a novel method that leverages text-to-image models to generate feature-aligned 2D quad images, from which pixel-wise directional fields are extracted. These fields are then lifted to the 3D surface through a two-stage interpolation scheme—combining intra-patch and cross-view weighted blending. A confidence-guided weighting mechanism is introduced to resolve directional ambiguities, enabling smooth extrapolation into occluded areas and supporting user sketch-based interaction. Experiments demonstrate that CrossLift produces quadrilateral meshes with superior semantic consistency on both organic and mechanical models, significantly outperforming existing approaches, and shows promise for applications in texture alignment and interactive design.

Abstract

We present CrossLift, a technique for computing cross fields on meshes guided by visual features in images. We leverage powerful text-to-image priors that are capable of synthesizing images of feature-aligned quad meshes in 2D. We extract this signal as explicit per-pixel directions in the 2D images, which we then back-project to the mesh surface. We aggregate these candidate surface directions by performing two smooth interpolations on the mesh surface (first within each view and second across multiple views). We propose custom confidence-based weights for the candidate directions in each interpolation that allow us to resolve conflicts between candidates on the same face and smoothly interpolate our field to occluded faces. Our method is modular and can be used with many different 2D visual priors. We show additional applications to texture-aligned quad meshing as well as interactive cross-field design using coarse, user-drawn lines as signal. We demonstrate the effectiveness of CrossLift on a diverse set of both organic and mechanical shapes and produce quad meshes that exhibit superior semantic alignment as compared to existing methods. Project page at: https://crosslift.github.io/

Problem

• cross fields
• quad meshing
• visual priors
• semantic alignment
• 3D reconstruction

Innovation

• cross field
• text-to-image prior
• quad meshing
• directional field lifting
• confidence-weighted interpolation