AuthorsMiguel Angel Bautista, Pengsheng Guo, Samira Abnar, Walter Talbott, Alexander Toshev, Zhuoyuan Chen, Laurent Dinh, Shuangfei Zhai, Hanlin Goh, Daniel Ulbricht, Afshin Dehghan, Josh Susskind
We introduce GAUDI, a generative model capable of capturing the distribution of complex and realistic 3D scenes that can be rendered immersively from a moving camera. We tackle this challenging problem with a scalable yet powerful approach, where we first optimize a latent representation that disentangles radiance fields and camera poses. This latent representation is then used to learn a generative model that enables both unconditional and conditional generation of 3D scenes. Our model generalizes previous works that focus on single objects by removing the assumption that the camera pose distribution can be shared across samples. We show that GAUDI obtains state-of-the-art performance in the unconditional generative setting across multiple datasets and allows for conditional generation of 3D scenes given conditioning variables like sparse image observations or text that describes the scene.
People have an innate capability to understand the 3D visual world and make predictions about how the world could look from different points of view, even when relying on few visual observations. We have this spatial reasoning ability because of the rich mental models of the visual world we develop over time. These mental models can be interpreted as a prior belief over which configurations of the visual world are most likely to be observed. In this case, a prior is a probability distribution over the 3D visual world.
Our research in machine learning breaks new ground every day.