3D scene generation from scene graphs and self-attention
<|reference_start|>3D scene generation from scene graphs and self-attention: 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%).<|reference_end|>
@article{bonazzi20243d,
    title={3D scene generation from scene graphs and self-attention},
    author={Pietro Bonazzi, Mengqi Wang, Diego Martin Arroyo, Fabian Manhardt,
  Nico Messikomer, Federico Tombari, Davide Scaramuzza},
    journal={arXiv preprint arXiv:2404.01887},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01887},
    primaryClass={cs.CV}
}