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首页> 外文会议>IEEE/CVF Conference on Computer Vision and Pattern Recognition >Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis
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Towards Unsupervised Learning of Generative Models for 3D Controllable Image Synthesis

机译：面向3D可控图像合成生成模型的无监督学习

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In recent years, Generative Adversarial Networks have achieved impressive results in photorealistic image synthesis. This progress nurtures hopes that one day the classical rendering pipeline can be replaced by efficient models that are learned directly from images. However, current image synthesis models operate in the 2D domain where disentangling 3D properties such as camera viewpoint or object pose is challenging. Furthermore, they lack an interpretable and controllable representation. Our key hypothesis is that the image generation process should be modeled in 3D space as the physical world surrounding us is intrinsically three-dimensional. We define the new task of 3D controllable image synthesis and propose an approach for solving it by reasoning both in 3D space and in the 2D image domain. We demonstrate that our model is able to disentangle latent 3D factors of simple multi-object scenes in an unsupervised fashion from raw images. Compared to pure 2D baselines, it allows for synthesizing scenes that are consistent wrt. changes in viewpoint or object pose. We further evaluate various 3D representations in terms of their usefulness for this challenging task.
机译：近年来，生成对抗网络在逼真的图像合成中取得了令人印象深刻的结果。这一进步孕育着希望有一天，经典的渲染管道可以被直接从图像中学习的高效模型所取代。但是，当前的图像合成模型在2D域中运行，在其中难以解开3D属性（例如相机视点或对象姿势）的挑战。此外，它们缺乏可解释和可控制的表示形式。我们的主要假设是，由于我们周围的物理世界本质上是三维的，因此应该在3D空间中对图像生成过程进行建模。我们定义了3D可控图像合成的新任务，并提出了一种通过在3D空间和2D图像域中进行推理来解决该问题的方法。我们证明了我们的模型能够以无人监督的方式从原始图像中解开简单多对象场景的潜在3D因子。与纯2D基准相比，它可以合成前后一致的场景。视点或物体姿势的变化。我们进一步评估各种3D表示形式对这项艰巨任务的有用性。

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