Scene Generation
46 papers with code • 3 benchmarks • 6 datasets
Libraries
Use these libraries to find Scene Generation models and implementations
Replica
VizDoom
AVD
CoDraw
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.
