๐[Preprint]:point_left:
Motion completion is a challenging and long-discussed problem, which is of great significance in film and game applications. For different motion completion scenarios (in-betweening, in-filling, and blending), most previous methods deal with the completion problems with case-by-case designs. In this work, we propose a simple but effective method to solve multiple motion completion problems under a unified framework and achieves a new state of the art accuracy under multiple evaluation settings. Inspired by the recent great success of attention-based models, we consider the completion as a sequence to sequence prediction problem. Our method consists of two modules - a standard transformer encoder with self-attention that learns long-range dependencies of input motions, and a trainable mixture embedding module that models temporal information and discriminates key-frames. Our method can run in a non-autoregressive manner and predict multiple missing frames within a single forward propagation in real time. We finally show the effectiveness of our method in music-dance applications.
With the help of Transformer, we achieve a new SOTA result on Lafan1 dataset.
Lengths = 30 | L2Q | L2P | NPSS |
---|---|---|---|
Zero-Vel | 1.51 | 6.60 | 0.2318 |
Interp. | 0.98 | 2.32 | 0.2013 |
ERD-QV | 0.69 | 1.28 | 0.1328 |
Ours | 0.61 | 1.10 | 0.1222 |
- Some results (blue appearaces represent keyframes):
We also evaluate our method on the Anidance dataset:
- Infilling on the test set (black skeletons are the keyframes):
(From Left to Right: Ours
, Interp
. and Ground Truth
)
- Infilling on random keyframes (keyframes are randomly chosen from the test set with a random order for simulating in-the-wild scenario):
(From Left to Right: Ours
, Interp
. and Ground Truth
)
Our method can also work on complex dance movement completion:
Coming soon
@misc{duan2021singleshot,
title={Single-Shot Motion Completion with Transformer},
author={Yinglin Duan and Tianyang Shi and Zhengxia Zou and Yenan Lin and Zhehui Qian and Bohan Zhang and Yi Yuan},
year={2021},
eprint={2103.00776},
archivePrefix={arXiv},
primaryClass={cs.CV}
}