3 citations · 7 across the 6 of their papers we have counts for
6 papers · 1 filter
GeoDiffusion: A Training-Free Framework for Accurate 3D Geometric Conditioning in Image Generation
Phillip Mueller, Talip Uenlue, Sebastian Schmidt +4
Precise geometric control in image generation is essential for engineering \& product design and creative industries to control 3D object features accurately in image space. Tradit…
A Mesh Is Worth 512 Numbers: Spectral-domain Diffusion Modeling for High-dimension Shape Generation
Jiajie Fan, Amal Trigui, Andrea Bonfanti +3
Recent advancements in learning latent codes derived from high-dimensional shapes have demonstrated impressive outcomes in 3D generative modeling. Traditionally, these approaches e…
NeuroNURBS: Learning Efficient Surface Representations for 3D Solids
Jiajie Fan, Babak Gholami, Thomas Bäck +1
Boundary Representation (B-Rep) is the de facto representation of 3D solids in Computer-Aided Design (CAD). B-Rep solids are defined with a set of NURBS (Non-Uniform Rational B-Spl…
Fréchet Denoised Distance: Enhancing Plausibility Evaluation for Generated Designs with Denoising Autoencoder
Jiajie Fan, Amal Trigui, Thomas Bäck +1
A great interest has arisen in using Deep Generative Models (DGM) for generative design. When assessing the quality of the generated designs, human designers focus more on structur…
On the Noise Scheduling for Generating Plausible Designs with Diffusion Models
Jiajie Fan, Laure Vuaille, Thomas Bäck +1
Deep Generative Models (DGMs) are widely used to create innovative designs across multiple industries, ranging from fashion to the automotive sector. In addition to generating imag…
Adversarial Latent Autoencoder with Self-Attention for Structural Image Synthesis
Jiajie Fan, Laure Vuaille, Hao Wang +1
Generative Engineering Design approaches driven by Deep Generative Models (DGM) have been proposed to facilitate industrial engineering processes. In such processes, designs often…