Dancing Avatar: Pose and Text-Guided Human Motion Videos Synthesis with
  Image Diffusion Model
Bosheng Qin Wentao Ye Qifan Yu Siliang Tang Yueting Zhuang
The rising demand for creating lifelike avatars in the digital realm has led
to an increased need for generating high-quality human videos guided by textual
descriptions and poses. We propose Dancing Avatar, designed to fabricate human
motion videos driven by poses and textual cues. Our approach employs a
pretrained T2I diffusion model to generate each video frame in an
autoregressive fashion. The crux of innovation lies in our adept utilization of
the T2I diffusion model for producing video frames successively while
preserving contextual relevance. We surmount the hurdles posed by maintaining
human character and clothing consistency across varying poses, along with
upholding the background's continuity amidst diverse human movements. To ensure
consistent human appearances across the entire video, we devise an intra-frame
alignment module. This module assimilates text-guided synthesized human
character knowledge into the pretrained T2I diffusion model, synergizing
insights from ChatGPT. For preserving background continuity, we put forth a
background alignment pipeline, amalgamating insights from segment anything and
image inpainting techniques. Furthermore, we propose an inter-frame alignment
module that draws inspiration from an auto-regressive pipeline to augment
temporal consistency between adjacent frames, where the preceding frame guides
the synthesis process of the current frame. Comparisons with state-of-the-art
methods demonstrate that Dancing Avatar exhibits the capacity to generate human
videos with markedly superior quality, both in terms of human and background
fidelity, as well as temporal coherence compared to existing state-of-the-art
approaches.