63 citations · 80 across the 12 of their papers we have counts for
15 papers
ICo3D: An Interactive Conversational 3D Virtual Human
Richard Shaw, Youngkyoon Jang, Athanasios Papaioannou +4
This work presents Interactive Conversational 3D Virtual Human (ICo3D), a method for generating an interactive, conversational, and photorealistic 3D human avatar. Based on multi-v…
GASPACHO: Gaussian Splatting for Controllable Humans and Objects
Aymen Mir, Arthur Moreau, Helisa Dhamo +3
We present GASPACHO, a method for generating photorealistic, controllable renderings of human-object interactions from multi-view RGB video. Unlike prior work that reconstructs onl…
FORCE: Physics-aware Human-object Interaction
Xiaohan Zhang, Bharat Lal Bhatnagar, Sebastian Starke +5
Interactions between human and objects are influenced not only by the object's pose and shape, but also by physical attributes such as object mass and surface friction. They introd…
SWinGS: Sliding Windows for Dynamic 3D Gaussian Splatting
Richard Shaw, Michal Nazarczuk, Jifei Song +4
Novel view synthesis has shown rapid progress recently, with methods capable of producing increasingly photorealistic results. 3D Gaussian Splatting has emerged as a promising meth…
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
Helisa Dhamo, Yinyu Nie, Arthur Moreau +4
3D head animation has seen major quality and runtime improvements over the last few years, particularly empowered by the advances in differentiable rendering and neural radiance fi…
Human Gaussian Splatting: Real-time Rendering of Animatable Avatars
Arthur Moreau, Jifei Song, Helisa Dhamo +3
This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos. While the classical approaches to model and render virtu…
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation
Azade Farshad, Yousef Yeganeh, Helisa Dhamo +2
Graph representation of objects and their relations in a scene, known as a scene graph, provides a precise and discernible interface to manipulate a scene by modifying the nodes or…
Object-aware Monocular Depth Prediction with Instance Convolutions
Enis Simsar, Evin Pınar Örnek, Fabian Manhardt +3
With the advent of deep learning, estimating depth from a single RGB image has recently received a lot of attention, being capable of empowering many different applications ranging…
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…
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…
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…
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…
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…
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…
Peeking Behind Objects: Layered Depth Prediction from a Single Image
Helisa Dhamo, Keisuke Tateno, Iro Laina +2
While conventional depth estimation can infer the geometry of a scene from a single RGB image, it fails to estimate scene regions that are occluded by foreground objects. This limi…