title: AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation
summary: This paper presents a method that can quickly adapt dynamic 3D avatars to
  arbitrary text descriptions of novel styles. Among existing approaches for
  avatar stylization, direct optimization methods can produce excellent results
  for arbitrary styles but they are unpleasantly slow. Furthermore, they require
  redoing the optimization process from scratch for every new input. Fast
  approximation methods using feed-forward networks trained on a large dataset of
  style images can generate results for new inputs quickly, but tend not to
  generalize well to novel styles and fall short in quality. We therefore
  investigate a new approach, AlteredAvatar, that combines those two approaches
  using the meta-learning framework. In the inner loop, the model learns to
  optimize to match a single target style well; while in the outer loop, the
  model learns to stylize efficiently across many styles. After training,
  AlteredAvatar learns an initialization that can quickly adapt within a small
  number of update steps to a novel style, which can be given using texts, a
  reference image, or a combination of both. We show that AlteredAvatar can
  achieve a good balance between speed, flexibility and quality, while
  maintaining consistency across a wide range of novel views and facial
  expressions.
paper_authors._id: 6476e1ca83d4fdaedde06ea7
paper_authors.name: Thu Nguyen-Phuoc
paper_authors._id: 6476e1ca83d4fdaedde06ea8
paper_authors.name: Gabriel Schwartz
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2023-06-02T12:34:16.522Z
paper_authors._id: 63b261b9d5f79ac67d2cace3
paper_authors.avatarUrl: /avatars/fa560a2e7732cf9e4cd85db3496fd6b2.svg
paper_authors.fullname: Gabriel Schwartz 
paper_authors.type: user
paper_authors.user: GBielXONE02
paper_authors._id: 6476e1ca83d4fdaedde06ea9
paper_authors.name: Yuting Ye
paper_authors._id: 6476e1ca83d4fdaedde06eaa
paper_authors.name: Stephen Lombardi
paper_authors._id: 6476e1ca83d4fdaedde06eab
paper_authors.name: Lei Xiao
paper_title: AlteredAvatar: Stylizing Dynamic 3D Avatars with Fast Style Adaptation
paper_summary: This paper presents a method that can quickly adapt dynamic 3D avatars to
  arbitrary text descriptions of novel styles. Among existing approaches for
  avatar stylization, direct optimization methods can produce excellent results
  for arbitrary styles but they are unpleasantly slow. Furthermore, they require
  redoing the optimization process from scratch for every new input. Fast
  approximation methods using feed-forward networks trained on a large dataset of
  style images can generate results for new inputs quickly, but tend not to
  generalize well to novel styles and fall short in quality. We therefore
  investigate a new approach, AlteredAvatar, that combines those two approaches
  using the meta-learning framework. In the inner loop, the model learns to
  optimize to match a single target style well; while in the outer loop, the
  model learns to stylize efficiently across many styles. After training,
  AlteredAvatar learns an initialization that can quickly adapt within a small
  number of update steps to a novel style, which can be given using texts, a
  reference image, or a combination of both. We show that AlteredAvatar can
  achieve a good balance between speed, flexibility and quality, while
  maintaining consistency across a wide range of novel views and facial
  expressions.
paper_ai_summary: AlteredAvatar combines direct optimization and fast approximation methods for avatar stylization using meta-learning to achieve a balance between speed, flexibility, and quality.
paper_ai_keywords: meta-learning direct optimization feed-forward networks avatar stylization style images text descriptions reference images update steps novel styles consistency novel views facial expressions