Scene Generation
62 papers with code • 5 benchmarks • 8 datasets
Libraries
Use these libraries to find Scene Generation models and implementationsMost implemented papers
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user.
SceneFormer: Indoor Scene Generation with Transformers
In contrast, we do not use any appearance information, and implicitly learn object relations using the self-attention mechanism of transformers.
Generative Adversarial Transformers
We introduce the GANformer, a novel and efficient type of transformer, and explore it for the task of visual generative modeling.
GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement
Moreover, object representations are often inferred using RNNs which do not scale well to large images or iterative refinement which avoids imposing an unnatural ordering on objects in an image but requires the a priori initialisation of a fixed number of object representations.
Context-aware Frame-Semantic Role Labeling
Frame semantic representations have been useful in several applications ranging from text-to-scene generation, to question answering and social network analysis.
Cross-View Image Synthesis using Conditional GANs
X-Fork architecture has a single discriminator and a single generator.
Probabilistic Neural Programmed Networks for Scene Generation
In this paper we address the text to scene image generation problem.
LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
Layout is important for graphic design and scene generation.
A Layer-Based Sequential Framework for Scene Generation with GANs
The visual world we sense, interpret and interact everyday is a complex composition of interleaved physical entities.
Auto-Encoding Progressive Generative Adversarial Networks For 3D Multi Object Scenes
The conventional 3D generative adversarial models are not efficient in generating multi object scenes, they usually tend to generate either one object or generate fuzzy results of multiple objects.