Scene Generation
115 papers with code • 6 benchmarks • 9 datasets
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Libraries
Use these libraries to find Scene Generation models and implementationsMost 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
We have witnessed rapid progress on 3D-aware image synthesis, leveraging recent advances in generative visual models and neural rendering.
LUMINOUS: Indoor Scene Generation for Embodied AI Challenges
However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts.
ROOT: VLM based System for Indoor Scene Understanding and Beyond
To train our SceneVLM, we collect over 610, 000 images from various public indoor datasets and implement a scene data generation pipeline with a semi-automated technique to establish relationships and estimate distances among indoor objects.
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
Recently there is an increasing interest in scene generation within the research community.
SceneGraphNet: Neural Message Passing for 3D Indoor Scene Augmentation
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
Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning.
Specifying Object Attributes and Relations in Interactive Scene Generation
We introduce a method for the generation of images from an input scene graph.
Semantic Bottleneck Scene Generation
For the former, we use an unconditional progressive segmentation generation network that captures the distribution of realistic semantic scene layouts.