Shape-conditioned Image Generation by Learning Latent Appearance Representation from Unpaired Data
Conditional image generation is effective for diverse tasks including training data synthesis for learning-based computer vision. However, despite the recent advances in generative adversarial networks (GANs), it is still a challenging task to generate images with detailed conditioning on object shapes. Existing methods for conditional image generation use category labels and/or keypoints and are only give limited control over object categories. In this work, we present SCGAN, an architecture to generate images with a desired shape specified by an input normal map. The shape-conditioned image generation task is achieved by explicitly modeling the image appearance via a latent appearance vector. The network is trained using unpaired training samples of real images and rendered normal maps. This approach enables us to generate images of arbitrary object categories with the target shape and diverse image appearances. We show the effectiveness of our method through both qualitative and quantitative evaluation on training data generation tasks.
Code (0)
등록된 구현이 없습니다.
Tasks
Conditional Image GenerationImage GenerationObjectSimilar Papers 제목 키워드 기반
A Variational U-Net for Conditional Appearance and Shape Generation
Deep generative models have demonstrated great performance in image synthesis. However, results deteriorate in case of spatial deformations, since they generate images of objects directly, rather than modeling the intric…
Conditional Image GenerationImage Generation3DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Models
We introduce 3DShape2VecSet, a novel shape representation for neural fields designed for generative diffusion models. Our shape representation can encode 3D shapes given as surface models or point clouds, and represents …
3D Shape RepresentationPoint Cloud CompletionShaRF: Shape-conditioned Radiance Fields from a Single View
We present a method for estimating neural scenes representations of objects given only a single image. The core of our method is the estimation of a geometric scaffold for the object and its use as a guide for the recons…
DisentanglementObjectGenerating Furry Cars: Disentangling Object Shape & Appearance across Multiple Domains
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that learns an intermediate distri…
DisentanglementObjectGenerating Furry Cars: Disentangling Object Shape and Appearance across Multiple Domains
We consider the novel task of learning disentangled representations of object shape and appearance across multiple domains (e.g., dogs and cars). The goal is to learn a generative model that learns an intermediate distr…
DisentanglementObject