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Neural Scene Graphs for Dynamic Scenes
Recent implicit neural rendering methods have demonstrated that it is po...
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A Number Sense as an Emergent Property of the Manipulating Brain
The ability to understand and manipulate numbers and quantities emerges ...
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Equivariant Neural Rendering
We propose a framework for learning neural scene representations directl...
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Finding Your (3D) Center: 3D Object Detection Using a Learned Loss
Massive semantic labeling is readily available for 2D images, but much h...
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Example-Guided Image Synthesis across Arbitrary Scenes using Masked Spatial-Channel Attention and Self-Supervision
Example-guided image synthesis has recently been attempted to synthesize...
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SceneSuggest: Context-driven 3D Scene Design
We present SceneSuggest: an interactive 3D scene design system providing...
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Example-Guided Scene Image Synthesis using Masked Spatial-Channel Attention and Patch-Based Self-Supervision
Example-guided image synthesis has been recently attempted to synthesize...
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Curiosity-driven 3D Scene Structure from Single-image Self-supervision
Previous work has demonstrated learning isolated 3D objects (voxel grids, point clouds, meshes, etc.) from 2D-only self-supervision. We here set out to extend this to entire 3D scenes made out of multiple objects, including their location, orientation and type, and the scenes illumination. Once learned, we can map arbitrary 2D images to 3D scene structure. We analyze why analysis-by-synthesis-like losses for supervision of 3D scene structure using differentiable rendering is not practical, as it almost always gets stuck in local minima of visual ambiguities. This can be overcome by a novel form of training: we use an additional network to steer the optimization itself to explore the full gamut of possible solutions i.e. to be curious, and hence, to resolve those ambiguities and find workable minima. The resulting system converts 2D images of different virtual or real images into complete 3D scenes, learned only from 2D images of those scenes.
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