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Found 7 papers, 2 papers with code
Gaussian Splatting Decoder for 3D-aware Generative Adversarial Networks
By training a decoder that maps implicit NeRF representations to explicit 3D Gaussian Splatting attributes, we can integrate the representational diversity and quality of 3D GANs into the ecosystem of 3D Gaussian Splatting for the first time.
X-maps: Direct Depth Lookup for Event-based Structured Light Systems
We present a new approach to direct depth estimation for Spatial Augmented Reality (SAR) applications using event cameras.
Compact 3D Scene Representation via Self-Organizing Gaussian Grids
In this paper, we introduce a compact scene representation organizing the parameters of 3D Gaussian Splatting (3DGS) into a 2D grid with local homogeneity, ensuring a drastic reduction in storage requirements without compromising visual quality during rendering.
Animating NeRFs from Texture Space: A Framework for Pose-Dependent Rendering of Human Performances
The extension to a controllable synthesis of dynamic human performances poses an exciting research question.
Animatable Virtual Humans: Learning pose-dependent human representations in UV space for interactive performance synthesis
We propose a novel representation of virtual humans for highly realistic real-time animation and rendering in 3D applications.
Imposing Temporal Consistency on Deep Monocular Body Shape and Pose Estimation
In detail, we derive parameters of a sequence of body models, representing shape and motion of a person, including jaw poses, facial expressions, and finger poses.
Going beyond Free Viewpoint: Creating Animatable Volumetric Video of Human Performances
Going beyond the application of free-viewpoint volumetric video, we allow re-animation and alteration of an actor's performance through (i) the enrichment of the captured data with semantics and animation properties and (ii) applying hybrid geometry- and video-based animation methods that allow a direct animation of the high-quality data itself instead of creating an animatable model that resembles the captured data.
