activity
20182021
most citedMIGS: Meta Image Generation from Scene Graphs

7 citations · 9 across the 3 of their papers we have counts for

collaborators

7 papers

cs.CV20217 cited

MIGS: Meta Image Generation from Scene Graphs

Azade Farshad, Sabrina Musatian, Helisa Dhamo +1

Generation of images from scene graphs is a promising direction towards explicit scene generation and manipulation. However, the images generated from the scene graphs lack quality…

cs.CV2021

Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs

Helisa Dhamo, Fabian Manhardt, Nassir Navab +1

Controllable scene synthesis consists of generating 3D information that satisfy underlying specifications. Thereby, these specifications should be abstract, i.e. allowing easy user…

cs.CV2021

Unconditional Scene Graph Generation

Sarthak Garg, Helisa Dhamo, Azade Farshad +3

Despite recent advancements in single-domain or single-object image generation, it is still challenging to generate complex scenes containing diverse, multiple objects and their in…

cs.CV2020

Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions

Johanna Wald, Helisa Dhamo, Nassir Navab +1

Scene understanding has been of high interest in computer vision. It encompasses not only identifying objects in a scene, but also their relationships within the given context. Wit…

cs.CV20202 cited

Semantic Image Manipulation Using Scene Graphs

Helisa Dhamo, Azade Farshad, Iro Laina +4

Image manipulation can be considered a special case of image generation where the image to be produced is a modification of an existing image. Image generation and manipulation hav…

cs.CV2019

Object-Driven Multi-Layer Scene Decomposition From a Single Image

Helisa Dhamo, Nassir Navab, Federico Tombari

We present a method that tackles the challenge of predicting color and depth behind the visible content of an image. Our approach aims at building up a Layered Depth Image (LDI) fr…