Scene Generation

46 papers with code • 3 benchmarks • 6 datasets

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Use these libraries to find Scene Generation models and implementations
3 papers

Most implemented papers

Funnel Activation for Visual Recognition

megvii-model/FunnelAct ECCV 2020

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 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

BerkeleyLearnVerify/Scenic 25 Sep 2018

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 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 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 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 ICCV 2019

We introduce a method for the generation of images from an input scene graph.

Semantic Bottleneck Scene Generation

azadis/SB-GAN 26 Nov 2019

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 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

Ha0Tang/LGGAN CVPR 2020

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.