Embroidery is a long‐standing and high‐quality approach to making logos and images on textiles. Nowadays, it can also be performed via automated machines that weave threads with high spatial accuracy. A characteristic feature of the appearance of the threads is a high degree of anisotropy. The anisotropic behavior is caused by depositing thin but long strings of thread. As a result, the stitched patterns convey both color and direction. Artists leverage this anisotropic behavior to enhance pure color images with textures, illusions of motion, or depth cues. However, designing colorful embroidery patterns with prescribed directionality is a challenging task, one usually requiring an expert designer. In this work, we propose an interactive algorithm that generates machine‐fabricable embroidery patterns from multi‐chromatic images equipped with user‐specified directionality fields. We cast the problem of finding a stitching pattern into vector theory. To find a suitable stitching pattern, we extract sources and sinks from the divergence field of the vector field extracted from the input and use them to trace streamlines. We further optimize the streamlines to guarantee a smooth and connected stitching pattern. The generated patterns approximate the color distribution constrained by the directionality field. To allow for further artistic control, the trade‐off between color match and directionality match can be interactively explored via an intuitive slider. We showcase our approach by fabricating several embroidery paths.
Figure 1: Given the assembly structure of the 153-brick LEGO Technic ROLLING CHASSIS (a), our method quantifies the rigidity of the structure (b1) and finds the worst-case external load configuration (yellow arrows) that maximally deforms it (c). A larger rigidity value indicates a more rigid assembly and thus greater resistance to external forces. After we strengthen the initial model as per the recommendation given by our method, the reinforced model (b2) has its rigidity value tripled, and undergoes less deformation than the initial one under the same loads that twist the model. The physical experiments (c) also show results that align with the analysis of our method.
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