Novel Pose Synthesis from few images
Abstract
This project aims to develop a novel human rendering model that generates animatable human scenes from a few images of a person with unseen identity, views, and poses. The proposed method differs from existing methods in that it can generalize to any input image for animatable human synthesis. Given a random pose sequence, the method synthesizes each target scene using a neural radiance field that is conditioned on a canonical representation and pose-aware pixel-aligned features, both of which can be obtained through deformation fields learned in a data-driven manner.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 22, 2024
- Source ID
- FA23862314118
Entities
People
- Jaegul Choo
Organizations
- Air Force Office of Scientific Research
- KAIST
- United States Air Force