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Found 6 papers, 1 papers with code
Neural Implicit Shape Editing using Boundary Sensitivity
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.
Localized Latent Updates for Fine-Tuning Vision-Language Models
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.
Intuitive Shape Editing in Latent Space
We evaluate our method by comparing to state-of-the-art data-driven shape editing methods.
Octree Transformer: Autoregressive 3D Shape Generation on Hierarchically Structured Sequences
Autoregressive models have proven to be very powerful in NLP text generation tasks and lately have gained popularity for image generation as well.
3D Shape Generation with Grid-based Implicit Functions
To remedy these issues, we propose to train the GAN on grids (i. e. each cell covers a part of a shape).
A Convolutional Decoder for Point Clouds using Adaptive Instance Normalization
In addition, we show that careful sampling is important both for the input geometry and in our point cloud generation process to improve results.
