![]() Moreover, other learned tools may be appended or prepended to the base pipeline to develop more imaginative tools. As shown in Figure 1, in contrast to the 2D mosaic, our approach generates a 3D LEGO® face from a single 2D image. However, we extend the Image2LEGO® pipeline to include the pre-trained Volumetric Regression Network (VRN) for single-image 3D reconstruction of faces. For instance, generating LEGO® models from pictures of one’s face is already an application of interest, but current work is limited to the generation of 2D LEGO® mosaics from images, such as that shown to the left of Figure 1 (generated by the commercial product called LEGO® Mosaic Maker ). Though we focus in this paper on our novel approach for multi-class object-image-to-lego construction, the same approach is extended to other creative applications by leveraging previous image-to-model work. We tackle the issues specific to constructing high-resolution real 3D LEGO® models such as color and hallow structures. A high-level demonstration of the full Image2LEGO® pipeline is presented in Figure Image2Lego: Customized LEGO® Set Generation from Images, where a gray-scale 2D photograph of an airplane is converted to a 3D LEGO® model, and the corresponding instructions and brick parts list are used to construct a physical LEGO® airplane build. As such, our work represents the first complete approach that allows users to generate real LEGO® sets from 2D images in a single pipeline. Our work has three sequential components: it (i) converts a 2D image to a latent representation, (ii) decodes the latent representation to a 3D voxel model, and (iii) applies an algorithm to transform the voxelized model to 3D LEGO® bricks. To make these creative possibilities accessible to all, we develop an end-to-end approach for producing LEGO® -type brick 3D models directly from 2D images. LEGO® bricks are extraordinarily flexible by nature and have been assembled into intricate and fantastical structures in many cases, and simplifying the process of constructing the more complex designs is an important step to maintain appeal for amateur builders and attract a new generation of LEGO® enthusiasts. For all but the most exceptional LEGO® engineers, however, dreams quickly outgrow skills, and constructing the complex images around them becomes too great a challenge. \wacvfinalcopy įor decades, LEGO® bricks have been a staple of entertainment for children and adults alike, offering the ability to construct anything one can imagine from simple building blocks. Finally, we test these automatically generated LEGO® sets by constructing physical builds using real LEGO® bricks. In order to demonstrate the broad applicability of our system, we generate step-by-step building instructions and animations for LEGO® models of objects and human faces. ![]() An octree architecture enables the flexibility to produce multiple resolutions to best fit a user’s creative vision or design needs. We demonstrate first-of-its-kind conversion of photographs to 3D LEGO® models. LEGO® models are obtained by algorithmic conversion of the 3D voxelized model to bricks. We design a novel solution to this problem that uses an octree-structured autoencoder trained on 3D voxelized models to obtain a feasible latent representation for model reconstruction, and a separate network trained to predict this latent representation from 2D images. In order to make this feat possible, we implement a system that generates a LEGO® brick model from 2D images. Although LEGO® sets have entertained generations of children and adults, the challenge of designing customized builds matching the complexity of real-world or imagined scenes remains too great for the average enthusiast.
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