AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion
  Models
Authors: name: Kwan Yun, name: Seokhyeon Hong, name: Chaelin Kim, name: Junyong Noh

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.