One-shot Human Motion Transfer via Occlusion-Robust Flow Prediction and Neural Texturing
Yuzhu Ji, Chuanxia Zheng, and Tat-Jen Cham

TL;DR
This paper introduces a unified framework for one-shot human motion transfer that combines multi-scale feature warping and neural texturing, improving robustness and accuracy in challenging scenarios with occlusions and complex motions.
Contribution
The proposed method uniquely integrates multi-scale feature warping with neural texture mapping, leveraging DensePose information and multi-modal training to enhance motion transfer quality.
Findings
Achieves competitive results on full and half-view body datasets.
Effectively handles challenging cases with self-occlusions.
Generalizes well across diverse motion scenarios.
Abstract
Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in results, it remains challenging for methods with 2D body landmarks, skeleton and semantic mask to accurately capture correspondences between source and driving poses due to the large variation in motion and articulation complexity. In addition, the accuracy and precision of DensePose degrade the image quality for neural-rendering-based methods. To address the limitations and by both considering the importance of appearance and geometry for motion transfer, in this work, we proposed a unified framework that combines multi-scale feature warping and neural texture mapping to recover better 2D appearance and 2.5D geometry, partly by exploiting the information from DensePose, yet adapting to its inherent limited…
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Taxonomy
TopicsHuman Pose and Action Recognition · Advanced Vision and Imaging · Human Motion and Animation
