UniMoGen: Universal Motion Generation
Aliasghar Khani, Arianna Rampini, Evan Atherton, Bruno Roy

TL;DR
UniMoGen is a versatile, skeleton-agnostic diffusion model for motion generation that outperforms existing methods and supports diverse characters, styles, and control features.
Contribution
We introduce UniMoGen, a UNet-based diffusion model capable of skeleton-agnostic motion generation across diverse characters without predefined joint limits.
Findings
Outperforms state-of-the-art methods on 100style dataset
Achieves high performance across different skeletons
Offers controllability and motion continuation features
Abstract
Motion generation is a cornerstone of computer graphics, animation, gaming, and robotics, enabling the creation of realistic and varied character movements. A significant limitation of existing methods is their reliance on specific skeletal structures, which restricts their versatility across different characters. To overcome this, we introduce UniMoGen, a novel UNet-based diffusion model designed for skeleton-agnostic motion generation. UniMoGen can be trained on motion data from diverse characters, such as humans and animals, without the need for a predefined maximum number of joints. By dynamically processing only the necessary joints for each character, our model achieves both skeleton agnosticism and computational efficiency. Key features of UniMoGen include controllability via style and trajectory inputs, and the ability to continue motions from past frames. We demonstrate…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsHuman Motion and Animation · Generative Adversarial Networks and Image Synthesis · 3D Shape Modeling and Analysis
