- Conference Paper
While recent generative models for 2D images achieve impressive visual results, they clearly lack the ability to perform 3D reasoning. This heavily restricts the degree of control over generated objects as well as the possible applications of such models. In this work, we bridge this gap by leveraging recent advances in differentiable rendering. We design a framework that can generate triangle meshes and associated high-resolution texture maps, using only 2D supervision from single-view natural images. A key contribution of our work is the encoding of the mesh and texture as 2D representations, which are semantically aligned and can be easily modeled by a 2D convolutional GAN. We demonstrate the efficacy of our method on Pascal3D+ Cars and CUB, both in an unconditional setting and in settings where the model is conditioned on class labels, attributes, and text. Finally, we propose an evaluation methodology that assesses the mesh and texture quality separately. Show more
Book titleAdvances in Neural Information Processing Systems 33
Pages / Article No.
Organisational unit09462 - Hofmann, Thomas / Hofmann, Thomas
176004 - Deep Learning for Generating Template Pictorial and Textual Representations (SNF)
NotesDue to the Coronavirus (COVID-19) the conference was conducted virtually.
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