FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis

Ke Fan¹, Junshu Tang¹, Weijian Cao², Ran Yi¹, Moran Li², Jingyu Gong¹, Jiangning Zhang², Yabiao Wang², Chengjie Wang^1,2, Lizhuang Ma¹,

Shanghai Jiao Tong University¹, Tencent Youtu Lab²

arXiv Code(Coming Soon) Data(Coming Soon)

Text-to-motion synthesis is a crucial task in computer vision. Existing methods are limited in their universality, as they are tailored for single-person or two-person scenarios and can not be applied to generate motions for more individuals. To achieve the number-free motion synthesis, this paper reconsiders motion generation and proposes to unify the single and multi-person motion by the conditional motion distribution. Furthermore, a generation module and an interaction module are designed for our FreeMotion framework to decouple the process of conditional motion generation and finally support the number-free motion synthesis. Besides, based on our framework, the current single-person motion spatial control method could be seamlessly integrated, achieving precise control of multi-person motion. Extensive experiments demonstrate the superior performance of our method and our capability to infer single and multi-human motions simultaneously.

Single Person Motion Generation

"A person receives a gesture and responds with equal intensity, but eventually backs away by one step and falls silent. "

"A person lowers their body into a squat position and holds out their camera towards others. "

"A person raises their leg and kicks towards another person."

"A person bows to others by bending their knees slightly."

"A person bends down to pick up an object."

"A person runs continuously with strides, and jumps up and downs excitedly, cheering and shouting encouragements."

Two Person Motion Generation

"The first one crouches to capture an image of the second one. "

"Two humans are performing taekwondo, executing face-to-face punches. "

"The first person is sitting on a chair and examining a document, when the second approaches whilst holding a cellphone. "

"Both people rotate their bodies and move rhythmetically while spinning around. "

"The two persons circle around clockwise, raise both hands, stretch the right hand forward to cross each other. "

More Person Motion Generation

"Three people walk in a circle. "

"Three people prepare to make a strike. "

"Three engage in a fierce boxing match, with aggressively exchanging blows while also defending themselves. "

"Three people are playing a game of tossing and catching a toy duck."

"Four acknowledge each other's presence with a polite greeting."

Applications

Reaction Generation

We can fix the first motion as condition and generate different motion reactions under various text.

"A person swings their fist towards others while the other one kicks back for the incoming punch. "

"A person swings their fist towards others while the other use their both hands to defend. "

"A person swings their fist towards others while the other one evades the incoming punch by stepping back. "

Spatial Control

We can generate the controllable multi-person motion under spatial signals.

"Two people run towards each other with joy and embrace each other."

"Three people are walking to get away from each other."

"Four people walks forward."

Diverse Generation

We can use the same text to generate diverse two-person motion results.

"The first person bows to apologize to the other person."

Comparisons

We compare against Intergen for two person motion generation. The synthesized motion by our proposed method are more consistent with the description.

Approach Overview

BibTeX

@article{fan2024freemotion
      title={FreeMotion: A Unified Framework for Number-free Text-to-Motion Synthesis}, 
      author={Ke Fan and Junshu Tang and Weijian Cao and Ran Yi and Moran Li and Jingyu Gong and Jiangning Zhang and Yabiao Wang and Chengjie Wang and Lizhuang Ma},
      year={2024},
      eprint={2405.15763},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}