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SIGGRAPH Asia 2023 · Neural & Generative · 共 30 篇
  • CLIPXPlore: Coupled CLIP and Shape Spaces for 3D Shape Exploration
  • Concept Decomposition for Visual Exploration and Inspiration
  • Content-based Search for Deep Generative Models
  • Diffusion-based Holistic Texture Rectification and Synthesis
  • Discontinuity-Aware 2D Neural Fields
  • Domain-Agnostic Tuning-Encoder for Fast Personalization of Text-To-Image Models
  • DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
  • Enhancing Diffusion Models with 3D Perspective Geometry Constraints
  • EXIM: A Hybrid Explicit-Implicit Representation for Text-Guided 3D Shape Generation
  • Face0: Instantaneously Conditioning a Text-to-Image Model on a Face
  • GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations
  • HyperDreamer: Hyper-Realistic 3D Content Generation and Editing from a Single Image
  • IconShop: Text-Guided Vector Icon Synthesis with Autoregressive Transformers
  • Interactive Story Visualization with Multiple Characters
  • Learning Gradient Fields for Scalable and Generalizable Irregular Packing
  • MatFusion: A Generative Diffusion Model for SVBRDF Capture
  • MyStyle++: A Controllable Personalized Generative Prior
  • Neural Field Convolutions by Repeated Differentiation
  • Neural Packing: from Visual Sensing to Reinforcement Learning
  • ProSpect: Prompt Spectrum for Attribute-Aware Personalization of Diffusion Models
  • Repurposing Diffusion Inpainters for Novel View Synthesis

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    GroomGen: A High-Quality Generative Hair Model Using Hierarchical Latent Representations

    Yuxiao Zhou, Menglei Chai, Alessandro Pepe, Markus Gross, Thabo Beeler

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    SIGGRAPH Asia 2023Neural & GenerativeHair ModelingGenerative ModelLatent RepresentationHierarchical LearningHair Synthesis
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