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Found 13 papers, 5 papers with code
FORCE: Dataset and Method for Intuitive Physics Guided Human-object Interaction
Our key insight is that human motion is dictated by the interrelation between the force exerted by the human and the perceived resistance.
SWAGS: Sampling Windows Adaptively for Dynamic 3D Gaussian Splatting
We model the dynamics of a scene using a tunable MLP, which learns the deformation field from a canonical space to a set of 3D Gaussians per frame.
HeadGaS: Real-Time Animatable Head Avatars via 3D Gaussian Splatting
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 fields.
Human Gaussian Splatting: Real-time Rendering of Animatable Avatars
This work addresses the problem of real-time rendering of photorealistic human body avatars learned from multi-view videos.
DisPositioNet: Disentangled Pose and Identity in Semantic Image Manipulation
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 the edges in the graph.
Object-aware Monocular Depth Prediction with Instance Convolutions
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 from path planning for robotics to computational cinematography.
MIGS: Meta Image Generation from Scene Graphs
We propose MIGS (Meta Image Generation from Scene Graphs), a meta-learning based approach for few-shot image generation from graphs that enables adapting the model to different scenes and increases the image quality by training on diverse sets of tasks.
Graph-to-3D: End-to-End Generation and Manipulation of 3D Scenes Using Scene Graphs
Scene graphs are representations of a scene, composed of objects (nodes) and inter-object relationships (edges), proven to be particularly suited for this task, as they allow for semantic control on the generated content.
Unconditional Scene Graph Generation
Scene graphs, composed of nodes as objects and directed-edges as relationships among objects, offer an alternative representation of a scene that is more semantically grounded than images.
Learning 3D Semantic Scene Graphs from 3D Indoor Reconstructions
In our work we focus on scene graphs, a data structure that organizes the entities of a scene in a graph, where objects are nodes and their relationships modeled as edges.
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Semantic Image Manipulation Using Scene Graphs
In our work, we address the novel problem of image manipulation from scene graphs, in which a user can edit images by merely applying changes in the nodes or edges of a semantic graph that is generated from the image.
Object-Driven Multi-Layer Scene Decomposition From a Single Image
Our approach aims at building up a Layered Depth Image (LDI) from a single RGB input, which is an efficient representation that arranges the scene in layers, including originally occluded regions.
Peeking Behind Objects: Layered Depth Prediction from a Single Image
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.
