Hair editing is a critical image synthesis task that aims to edit hair color and hairstyle using textdescriptions orreference images, while preserving irrelevant attributes(e.g.,identity,background,cloth).

Photoswap is able to maintain the same pose with a decent style preservation.

One of core differences among various deep GCP methods is post-normalization for covariance representations, which plays a crucial role in final performance.

As a crucial step for analyzing human actions, hand-object interaction understanding is researchworthy in a broad range of applications related to virtual or augmented reality. Current works largely focus on hand-object pose estimation (HOPE) [16, 19, 21], which aims to capture the pose configuration of the given hand-object image.

Parametric dimensionality reduction methods have gained prominence for their ability togeneralize tounseen datasets, anadvantage that traditional approaches typically lack. Despite their growing popularity, there remains a prevalent misconception among practitioners about the equivalence in performance between parametric and non-parametric methods. Here, we showthat these methods are not equivalent - parametric methods retain global structure but lose significant localdetails.