Pose Transfer

Pose transfer, a subfield of computer vision and computer graphics, aims to realistically transfer the pose of one person or object onto another while preserving the original identity. Current research focuses on improving the accuracy and realism of pose transfer, particularly addressing artifacts and distortions, using techniques like conditional inpainting, adversarial learning, and diffusion models, often incorporating keypoint-based representations or mesh-based approaches. These advancements have significant implications for applications such as virtual try-on, animation, and 3D modeling, enabling more efficient and realistic content creation.