3D scene generation from scene graphs and self-attention

• URL: http://arxiv.org/abs/2404.01887v3
• Date: Wed, 24 Apr 2024 03:13:50 GMT
• Title: 3D scene generation from scene graphs and self-attention
• Authors: Pietro Bonazzi, Mengqi Wang, Diego Martin Arroyo, Fabian Manhardt, Nico Messikomer, Federico Tombari, Davide Scaramuzza,
• Abstract summary: We present a variant of the conditional variational autoencoder (cVAE) model to synthesize 3D scenes from scene graphs and floor plans. We exploit the properties of self-attention layers to capture high-level relationships between objects in a scene.
• Score: 51.49886604454926
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Synthesizing realistic and diverse indoor 3D scene layouts in a controllable fashion opens up applications in simulated navigation and virtual reality. As concise and robust representations of a scene, scene graphs have proven to be well-suited as the semantic control on the generated layout. We present a variant of the conditional variational autoencoder (cVAE) model to synthesize 3D scenes from scene graphs and floor plans. We exploit the properties of self-attention layers to capture high-level relationships between objects in a scene, and use these as the building blocks of our model. Our model, leverages graph transformers to estimate the size, dimension and orientation of the objects in a room while satisfying relationships in the given scene graph. Our experiments shows self-attention layers leads to sparser (7.9x compared to Graphto3D) and more diverse scenes (16%).

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