Tag: score-distillation-sampling

• Word-As-Image for Semantic Typography (13 Oct 2023)
  

  This is my reading note for Word-As-Image for Semantic Typography. This paper utilized the differential rendering for vector graph to train a diffusion model to generate vector graph for a given text. Check my note for related paper in # VectorFusion Text-to-SVG by Abstracting Pixel-Based Diffusion Models

• VectorFusion Text-to-SVG by Abstracting Pixel-Based Diffusion Models (12 Oct 2023)
  

  This is my reading note for VectorFusion: Text-to-SVG by Abstracting Pixel-Based Diffusion Models. This paper utilized the differential rendering for vector graph to train a diffusion model to generate vector graph for a given text.

• DreamFusion Text-to-3D using 2D Diffusion (01 Sep 2023)
  

  This is my reading note on DreamFusion: Text-to-3D using 2D Diffusion. This paper proposes a method (score distillation sampling or SDS) to distill a pre-trained text to image diffusion model to a 3D model. The 3D model, which is based on NERF, is trained per text prompt.

• Diff-Instruct A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models (28 Aug 2023)
  

  This is my reading note on Diff-Instruct: A Universal Approach for Transferring Knowledge From Pre-trained Diffusion Models. The paper explains the theory of using a pre-trained diffusion model to guide the training of a generator model.it shows that both DreamFusion and GAN are a special case of it: score distillation sampling (SDS) from DreamFusion uses Dirac distribution to represent the generator while GAN learns a discriminator to represents the distribution of data. To this end, it proposes IKL, which is tailored for DMs by calculating the integral of the KL divergence along a diffusion process (instead of a single step), which we show to be more robust in comparing distributions with misaligned supports.

• ProlificDreamer High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation (20 Aug 2023)
  

  This is my reading note on ProlificDreamer High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation. This method proposes variational score sampling to replace score distillation sampling to improve the details of text to image or text to 3D models. Project page: https://ml.cs.tsinghua.edu.cn/prolificdreamer/

• MeshDiffusion Score-based Generative 3D Mesh Modeling (02 Jul 2023)
  

  This is my reading note for MeshDiffusion: Score-based Generative 3D Mesh Modeling. This paper represents the 3D mesh as a reformed tetrahedral which is defined on a regular 3D grid with 4 channel features: 3D positional deformation of the vertex and signed distance function values to define the surface.