2 citations · 2 across the 6 of their papers we have counts for
6 papers
Neural Implicit Shape Editing using Boundary Sensitivity
Arturs Berzins, Moritz Ibing, Leif Kobbelt
Neural fields are receiving increased attention as a geometric representation due to their ability to compactly store detailed and smooth shapes and easily undergo topological chan…
Localized Latent Updates for Fine-Tuning Vision-Language Models
Moritz Ibing, Isaak Lim, Leif Kobbelt
Although massive pre-trained vision-language models like CLIP show impressive generalization capabilities for many tasks, still it often remains necessary to fine-tune them for imp…
Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences
Moritz Ibing, Gregor Kobsik, Leif Kobbelt
Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well. However, they have seen limited u…
Intuitive Shape Editing in Latent Space
Tim Elsner, Moritz Ibing, Victor Czech +2
The use of autoencoders for shape editing or generation through latent space manipulation suffers from unpredictable changes in the output shape. Our autoencoder-based method enabl…
3D Shape Generation with Grid-based Implicit Functions
Moritz Ibing, Isaak Lim, Leif Kobbelt
Previous approaches to generate shapes in a 3D setting train a GAN on the latent space of an autoencoder (AE). Even though this produces convincing results, it has two major shortc…
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization
Isaak Lim, Moritz Ibing, Leif Kobbelt
Automatic synthesis of high quality 3D shapes is an ongoing and challenging area of research. While several data-driven methods have been proposed that make use of neural networks…