Christopher Wewer
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View article: Spatial Reasoners for Continuous Variables in Any Domain
Spatial Reasoners for Continuous Variables in Any Domain Open
We present Spatial Reasoners, a software framework to perform spatial reasoning over continuous variables with generative denoising models. Denoising generative models have become the de-facto standard for image generation, due to their ef…
View article: Spurfies: Sparse Surface Reconstruction using Local Geometry Priors
Spurfies: Sparse Surface Reconstruction using Local Geometry Priors Open
We introduce Spurfies, a novel method for sparse-view surface reconstruction that disentangles appearance and geometry information to utilize local geometry priors trained on synthetic data. Recent research heavily focuses on 3D reconstruc…
View article: Sp2360: Sparse-view 360 Scene Reconstruction using Cascaded 2D Diffusion Priors
Sp2360: Sparse-view 360 Scene Reconstruction using Cascaded 2D Diffusion Priors Open
We aim to tackle sparse-view reconstruction of a 360 3D scene using priors from latent diffusion models (LDM). The sparse-view setting is ill-posed and underconstrained, especially for scenes where the camera rotates 360 degrees around a p…
View article: latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction
latentSplat: Autoencoding Variational Gaussians for Fast Generalizable 3D Reconstruction Open
We present latentSplat, a method to predict semantic Gaussians in a 3D latent space that can be splatted and decoded by a light-weight generative 2D architecture. Existing methods for generalizable 3D reconstruction either do not scale to …
View article: Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation
Neural Point Cloud Diffusion for Disentangled 3D Shape and Appearance Generation Open
Controllable generation of 3D assets is important for many practical applications like content creation in movies, games and engineering, as well as in AR/VR. Recently, diffusion models have shown remarkable results in generation quality o…
View article: Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction
Neural Parametric Gaussians for Monocular Non-Rigid Object Reconstruction Open
Reconstructing dynamic objects from monocular videos is a severely underconstrained and challenging problem, and recent work has approached it in various directions. However, owing to the ill-posed nature of this problem, there has been no…
View article: SimNP: Learning Self-Similarity Priors Between Neural Points
SimNP: Learning Self-Similarity Priors Between Neural Points Open
Existing neural field representations for 3D object reconstruction either (1) utilize object-level representations, but suffer from low-quality details due to conditioning on a global latent code, or (2) are able to perfectly reconstruct t…
View article: Dynamic Knowledge Graphs for Continual Learning of Embeddings
Dynamic Knowledge Graphs for Continual Learning of Embeddings Open
These datasets are generated from real world usecases. They are treated as Knowledge graphs and include 20 snapshots, where between two snapshots there are 10% added links and 10% deleted links, making the first and last snapshot non-overl…
View article: Dynamic Knowledge Graphs for Continual Learning of Embeddings
Dynamic Knowledge Graphs for Continual Learning of Embeddings Open
These datasets are generated from real world usecases. They are treated as Knowledge graphs and include 20 snapshots, where between two snapshots there are 10% added links and 10% deleted links, making the first and last snapshot non-overl…
View article: Dynamic Knowledge Graphs for Continual Learning of Embeddings
Dynamic Knowledge Graphs for Continual Learning of Embeddings Open
These datasets are generated from real world usecases. They are treated as Knowledge graphs and include 20 snapshots, where between two snapshots there are 10% added links and 10% deleted links, making the first and last snapshot non-overl…
View article: Updating Embeddings for Dynamic Knowledge Graphs
Updating Embeddings for Dynamic Knowledge Graphs Open
Data in Knowledge Graphs often represents part of the current state of the real world. Thus, to stay up-to-date the graph data needs to be updated frequently. To utilize information from Knowledge Graphs, many state-of-the-art machine lear…