# SILK: Smooth InterpoLation frameworK for motion in-betweening A Simplified Computational Approach

arXiv: https://arxiv.org/abs/2506.09075 · HTML: https://mlsanity.com/paper/2506.09075/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 47 references that are in the map.
- First posted on arXiv 9 Jun 2025.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Human Motion Diffusion Model](https://mlsanity.com/paper/2209.14916/) (2022)
- [On human motion prediction using recurrent neural networks](https://mlsanity.com/paper/1705.02445/) (2017)
- [Robust Motion In-betweening](https://mlsanity.com/paper/2102.04942/) (2021)
- [Guided Motion Diffusion for Controllable Human Motion Synthesis](https://mlsanity.com/paper/2305.12577/) (2023)
- [Listen, Denoise, Action! Audio-Driven Motion Synthesis with Diffusion Models](https://mlsanity.com/paper/2211.09707/) (2022)
- [Skeleton-Aware Networks for Deep Motion Retargeting](https://mlsanity.com/paper/2005.05732/) (2020)
- [Convolutional Autoencoders for Human Motion Infilling](https://mlsanity.com/paper/2010.11531/) (2020)
- [NeMF: Neural Motion Fields for Kinematic Animation](https://mlsanity.com/paper/2206.03287/) (2022)
- [Recurrent Transition Networks for Character Locomotion](https://mlsanity.com/paper/1810.02363/) (2018)
- [Task-Generic Hierarchical Human Motion Prior using VAEs](https://mlsanity.com/paper/2106.04004/) (2021)
- [Real-time Controllable Motion Transition for Characters](https://mlsanity.com/paper/2205.02540/) (2022)
- [Conditional Motion In-betweening](https://mlsanity.com/paper/2202.04307/) (2022)
- [Motion In-Betweening with Phase Manifolds](https://mlsanity.com/paper/2308.12751/) (2023)
- [Generative Tweening: Long-term Inbetweening of 3D Human Motions](https://mlsanity.com/paper/2005.08891/) (2020)
- [Motion Inbetweening via Deep Delta-Interpolator](https://mlsanity.com/paper/2201.06701/) (2022)
- [Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation](https://mlsanity.com/paper/2303.14926/) (2023)
- [Towards Robust Direction Invariance in Character Animation](https://mlsanity.com/paper/1907.06790/) (2019)
- [A Unified Masked Autoencoder with Patchified Skeletons for Motion Synthesis](https://mlsanity.com/paper/2308.07301/) (2023)
- [Real-time Diverse Motion In-betweening with Space-time Control](https://mlsanity.com/paper/2410.00270/) (2024)

## Built on by (most cited first, 1 of 1)

- [Adaptive Interpolation-Synthesis for Motion In-Betweening on Keyframe-Based Animation](https://mlsanity.com/paper/2605.02742/) (2026)

## Abstract (arXiv)

Motion in-betweening is a crucial tool for animators, enabling intricate control over pose-level details in each keyframe. Recent machine learning solutions for motion in-betweening rely on complex models, incorporating skeleton-aware architectures or requiring multiple modules and training steps. In this work, we introduce a simple yet effective Transformer-based framework, employing a single Transformer encoder to synthesize realistic motions for motion in-betweening tasks. We find that data modeling choices play a significant role in improving in-betweening performance. Among others, we show that increasing data volume can yield equivalent or improved motion transitions, that the choice of pose representation is vital for achieving high-quality results, and that incorporating velocity input features enhances animation performance. These findings challenge the assumption that model complexity is the primary determinant of animation quality and provide insights into a more data-centric approach to motion interpolation. Additional videos and supplementary material are available at https://silk-paper.github.io.