StickMotion: Generating 3D Human Motions by Drawing a Stickman
This work addresses the problem of inefficient and inaccurate motion generation for users in text-to-motion applications, offering a more user-friendly and effective solution.
The paper tackles the challenge of accurately generating 3D human motions from textual descriptions by introducing StickMotion, a diffusion-based network that uses stickman drawings for global and local control, resulting in users saving about 51.5% of time in generating motions consistent with their imagination.
Text-to-motion generation, which translates textual descriptions into human motions, has been challenging in accurately capturing detailed user-imagined motions from simple text inputs. This paper introduces StickMotion, an efficient diffusion-based network designed for multi-condition scenarios, which generates desired motions based on traditional text and our proposed stickman conditions for global and local control of these motions, respectively. We address the challenges introduced by the user-friendly stickman from three perspectives: 1) Data generation. We develop an algorithm to generate hand-drawn stickmen automatically across different dataset formats. 2) Multi-condition fusion. We propose a multi-condition module that integrates into the diffusion process and obtains outputs of all possible condition combinations, reducing computational complexity and enhancing StickMotion's performance compared to conventional approaches with the self-attention module. 3) Dynamic supervision. We empower StickMotion to make minor adjustments to the stickman's position within the output sequences, generating more natural movements through our proposed dynamic supervision strategy. Through quantitative experiments and user studies, sketching stickmen saves users about 51.5% of their time generating motions consistent with their imagination. Our codes, demos, and relevant data will be released to facilitate further research and validation within the scientific community.