title: StyleSplat: 3D Object Style Transfer with Gaussian Splatting
summary: Recent advancements in radiance fields have opened new avenues for creating
  high-quality 3D assets and scenes. Style transfer can enhance these 3D assets
  with diverse artistic styles, transforming creative expression. However,
  existing techniques are often slow or unable to localize style transfer to
  specific objects. We introduce StyleSplat, a lightweight method for stylizing
  3D objects in scenes represented by 3D Gaussians from reference style images.
  Our approach first learns a photorealistic representation of the scene using 3D
  Gaussian splatting while jointly segmenting individual 3D objects. We then use
  a nearest-neighbor feature matching loss to finetune the Gaussians of the
  selected objects, aligning their spherical harmonic coefficients with the style
  image to ensure consistency and visual appeal. StyleSplat allows for quick,
  customizable style transfer and localized stylization of multiple objects
  within a scene, each with a different style. We demonstrate its effectiveness
  across various 3D scenes and styles, showcasing enhanced control and
  customization in 3D creation.
paper_authors._id: 6694866b8cffcbe1be0eb717
paper_authors.name: Sahil Jain
paper_authors._id: 6694866b8cffcbe1be0eb718
paper_authors.name: Avik Kuthiala
paper_authors._id: 6694866b8cffcbe1be0eb719
paper_authors.name: Prabhdeep Singh Sethi
paper_authors._id: 6694866b8cffcbe1be0eb71a
paper_authors.name: Prakanshul Saxena
paper_title: StyleSplat: 3D Object Style Transfer with Gaussian Splatting
paper_summary: Recent advancements in radiance fields have opened new avenues for creating
  high-quality 3D assets and scenes. Style transfer can enhance these 3D assets
  with diverse artistic styles, transforming creative expression. However,
  existing techniques are often slow or unable to localize style transfer to
  specific objects. We introduce StyleSplat, a lightweight method for stylizing
  3D objects in scenes represented by 3D Gaussians from reference style images.
  Our approach first learns a photorealistic representation of the scene using 3D
  Gaussian splatting while jointly segmenting individual 3D objects. We then use
  a nearest-neighbor feature matching loss to finetune the Gaussians of the
  selected objects, aligning their spherical harmonic coefficients with the style
  image to ensure consistency and visual appeal. StyleSplat allows for quick,
  customizable style transfer and localized stylization of multiple objects
  within a scene, each with a different style. We demonstrate its effectiveness
  across various 3D scenes and styles, showcasing enhanced control and
  customization in 3D creation.
paper_ai_summary: StyleSplat efficiently applies localized style transfer to multiple 3D objects within scenes using 3D Gaussian splatting and spherical harmonic coefficients alignment.
paper_ai_keywords: radiance fields 3D Gaussians StyleSplat photorealistic representation spherical harmonic coefficients nearest-neighbor feature matching loss