no code implementations • 27 Jun 2019 • Isaak Lim, Moritz Ibing, Leif Kobbelt
In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results.
no code implementations • CVPR 2021 • Moritz Ibing, Isaak Lim, Leif Kobbelt
To remedy these issues, we propose to train the GAN on grids (i. e. each cell covers a part of a shape).
no code implementations • 24 Nov 2021 • Tim Elsner, Moritz Ibing, Victor Czech, Julius Nehring-Wirxel, Leif Kobbelt
We evaluate our method by comparing to state-of-the-art data-driven shape editing methods.
1 code implementation • 24 Nov 2021 • 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.
no code implementations • 13 Dec 2022 • 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 improved performance on specific datasets.
no code implementations • 24 Apr 2023 • Arturs Berzins, Moritz Ibing, Leif Kobbelt
Furthermore, we show how boundary sensitivity helps to optimize and constrain objectives (such as surface area and volume), which are difficult to compute without first converting to another representation, such as a mesh.