Scene Generation
62 papers with code • 5 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Scene Generation
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Libraries
Use these libraries to find Scene Generation models and implementations
Replica
VizDoom
AVD
CoDraw
OSM
GoogleEarth
InstaOrder
3D FRONT HUMAN
Most implemented papers
Funnel Activation for Visual Recognition
We present a conceptually simple but effective funnel activation for image recognition tasks, called Funnel activation (FReLU), that extends ReLU and PReLU to a 2D activation by adding a negligible overhead of spatial condition.
pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis
marcoamonteiro/pi-GAN
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CVPR 2021
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering.
Scenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning.
LayoutVAE: Stochastic Scene Layout Generation From a Label Set
kampta/DeepLayout
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ICCV 2019
Recently there is an increasing interest in scene generation within the research community.
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
yzhou359/3DIndoor-SceneGraphNet
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ICCV 2019
In this paper we propose a neural message passing approach to augment an input 3D indoor scene with new objects matching their surroundings.
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
applied-ai-lab/genesis
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ICLR 2020
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning.
Specifying Object Attributes and Relations in Interactive Scene Generation
ashual/scene_generation
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ICCV 2019
We introduce a method for the generation of images from an input scene graph.
Semantic Bottleneck Scene Generation
azadis/SB-GAN
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For the former, we use an unconditional progressive segmentation generation network that captures the distribution of realistic semantic scene layouts.
Learning Canonical Representations for Scene Graph to Image Generation
roeiherz/CanonicalSg2Im
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ECCV 2020
Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images.
Local Class-Specific and Global Image-Level Generative Adversarial Networks for Semantic-Guided Scene Generation
To tackle this issue, in this work we consider learning the scene generation in a local context, and correspondingly design a local class-specific generative network with semantic maps as a guidance, which separately constructs and learns sub-generators concentrating on the generation of different classes, and is able to provide more scene details.
