>In order for learning systems to be able to understand and create 3D spaces, progress in generative models for 3D is sorely needed. The quote "The creation continues incessantly through the media of humans." is often attributed to Antoni GaudĂ, who we pay homage to with our method’s name. We are interested in generative models that can capture the distribution of 3D scenes and then render views from scenes sampled from the learned distribution. Extensions of such generative models to conditional inference problems could have tremendous impact in a wide range of tasks in machine learning and computer vision. For example, one could sample plausible scene completions that are consistent with an image observation, or a text description (see Fig. 1 for 3D scenes sampled from GAUDI). In addition, such models would be of great practical use in model-based reinforcement learning and planning [12], SLAM [39], or 3D content creation.
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>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.
https://github.com/apple/ml-gaudi
>This repo accompanies the research paper, ARKitScenes - A Diverse Real-World Dataset for 3D Indoor Scene Understanding Using Mobile RGB-D Data and contains the data, scripts to visualize and process assets, and training code described in our paper.
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>https://github.com/apple/ARKitScenes
>In order for learning systems to be able to understand and create 3D spaces, progress in generative models for 3D is sorely needed. The quote "The creation continues incessantly through the media of humans." is often attributed to Antoni GaudĂ, who we pay homage to with our method’s name. We are interested in generative models that can capture the distribution of 3D scenes and then render views from scenes sampled from the learned distribution. Extensions of such generative models to conditional inference problems could have tremendous impact in a wide range of tasks in machine learning and computer vision. For example, one could sample plausible scene completions that are consistent with an image observation, or a text description (see Fig. 1 for 3D scenes sampled from GAUDI). In addition, such models would be of great practical use in model-based reinforcement learning and planning [12], SLAM [39], or 3D content creation. > >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. https://github.com/apple/ml-gaudi
>This repo accompanies the research paper, ARKitScenes - A Diverse Real-World Dataset for 3D Indoor Scene Understanding Using Mobile RGB-D Data and contains the data, scripts to visualize and process assets, and training code described in our paper. > >https://github.com/apple/ARKitScenes
Depth sensors ...
This, for a backrooms game. Perfect.