Human Motion Aware Text-to-Video Generation with Explicit Camera Control

Taehoon Kim, Chanhee Kang, JaeHyuk Park, Daun Jeong, ChangHee Yang, Suk-Ju Kang, Kyeongbo Kong

Abstract: With the rise in expectations related to generative models, text-to-video (T2V) models are being actively studied. Existing text-to-video models have limitations such as in generating complex movements replicating human motions. These model often generate unintended human motions, and the scale of the subject is incorrect. To overcome these limitations and generate high-quality videos that depict human motion under plausible viewing angles, we propose a two stage framework in this study. In the first stage a text-driven human motion generation network generates three-dimensional (3D) human motion from input text prompts and then motion-to-skeleton projection module projects generated motions onto a two-dimensional (2D) skeleton. In the second stage, the projected skeletons are used to generate a video in which the movements of a subject are well-represented. We demonstrated that the proposed framework quantitatively and qualitatively outperforms the existing T2V models. Previously reported human motion generation models use texts only or texts and human skeletons. However, our framework only uses texts and outputs a video related to human motion. Moreover, our framework benefits from using skeleton as an additional condition in the text-to-human motion generation networks. To the best of our knowledge, our framework is the first of its kind that uses text-driven human motion generation networks to generate high-quality videos related to human motions. The corresponding codes are available at https://github.com/CSJasper/HMTV.