title: AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion
    Models
summary: Despite recent advancements in learning-based motion in-betweening, a key
  limitation has been overlooked: the requirement for character-specific
  datasets. In this work, we introduce AnyMoLe, a novel method that addresses
  this limitation by leveraging video diffusion models to generate motion
  in-between frames for arbitrary characters without external data. Our approach
  employs a two-stage frame generation process to enhance contextual
  understanding. Furthermore, to bridge the domain gap between real-world and
  rendered character animations, we introduce ICAdapt, a fine-tuning technique
  for video diffusion models. Additionally, we propose a ``motion-video
  mimicking'' optimization technique, enabling seamless motion generation for
  characters with arbitrary joint structures using 2D and 3D-aware features.
  AnyMoLe significantly reduces data dependency while generating smooth and
  realistic transitions, making it applicable to a wide range of motion
  in-betweening tasks.
paper_authors._id: 67d15c5bac9173a540aec890
paper_authors.name: Kwan Yun
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-12T10:44:58.153Z
paper_authors._id: 639d445524af4747d8d2af52
paper_authors.avatarUrl: /avatars/6ca870edc993fd3db6b0db4e4848ef7a.svg
paper_authors.fullname: kwan yun
paper_authors.type: user
paper_authors.user: kwanY
paper_authors._id: 67d15c5bac9173a540aec891
paper_authors.name: Seokhyeon Hong
paper_authors._id: 67d15c5bac9173a540aec892
paper_authors.name: Chaelin Kim
paper_authors._id: 67d15c5bac9173a540aec893
paper_authors.name: Junyong Noh
paper_title: AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion
    Models
paper_summary: Despite recent advancements in learning-based motion in-betweening, a key
  limitation has been overlooked: the requirement for character-specific
  datasets. In this work, we introduce AnyMoLe, a novel method that addresses
  this limitation by leveraging video diffusion models to generate motion
  in-between frames for arbitrary characters without external data. Our approach
  employs a two-stage frame generation process to enhance contextual
  understanding. Furthermore, to bridge the domain gap between real-world and
  rendered character animations, we introduce ICAdapt, a fine-tuning technique
  for video diffusion models. Additionally, we propose a ``motion-video
  mimicking'' optimization technique, enabling seamless motion generation for
  characters with arbitrary joint structures using 2D and 3D-aware features.
  AnyMoLe significantly reduces data dependency while generating smooth and
  realistic transitions, making it applicable to a wide range of motion
  in-betweening tasks.
paper_ai_keywords: video diffusion models motion in-between frames two-stage frame generation contextual understanding ICAdapt motion-video mimicking optimization technique 2D and 3D-aware features