论文标题
Infinitenature-Zero：从单个图像中学习自然场景的永久视图
InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images
论文作者
论文摘要
我们提出了一种学习方法，可以从单个视图开始生成自然场景的无界飞行视频，在该视图中，从单个照片集中学习了这种功能，而无需每个场景的相机姿势甚至多个视图。为了实现这一目标，我们提出了一种新颖的自我监视的视图生成训练范式，在这里我们采样和渲染虚拟摄像头轨迹，包括循环轨迹，使我们的模型可以从单个视图集合中学习稳定的视图生成。在测试时，尽管在训练过程中从未见过视频，但我们的方法仍可以拍摄单个图像，并产生长的相机轨迹，其中包括数百个新视图，具有现实和多样化的内容。我们将我们的方法与最新的监督视图生成方法进行了比较，该方法需要构成多视频视频，并展示了卓越的性能和综合质量。
We present a method for learning to generate unbounded flythrough videos of natural scenes starting from a single view, where this capability is learned from a collection of single photographs, without requiring camera poses or even multiple views of each scene. To achieve this, we propose a novel self-supervised view generation training paradigm, where we sample and rendering virtual camera trajectories, including cyclic ones, allowing our model to learn stable view generation from a collection of single views. At test time, despite never seeing a video during training, our approach can take a single image and generate long camera trajectories comprised of hundreds of new views with realistic and diverse content. We compare our approach with recent state-of-the-art supervised view generation methods that require posed multi-view videos and demonstrate superior performance and synthesis quality.
