In this paper, we introduce TIVE, a Toolbox for Identifying Video instance segmentation
Errors. By directly operating output prediction files, TIVE defines isolated error types and …

Temporal modeling and spatio-temporal collaboration are pivotal techniques for video-
based human pose estimation. Most state-of-the-art methods adopt optical flow or temporal …

Human-centric perception is the core of diverse computer vision tasks and has been a long-
standing research focus. However, previous research studied these human-centric tasks …

In this paper, we explore reconstructing high-quality clothed 3D humans from a single RGB-
D image, assuming that virtual humans can be represented by front-view and back-view …

Human parsing is a fine-grained semantic segmentation task, which needs to understand
human semantic parts. Most existing methods model human parsing as a general semantic …

Instance-level human parsing remains challenging due to the similarity between human
instances and background, complex interactions, and various poses. Aiming at assigning …

In recent years, multi‐human parsing has become a focal point in research, yet prevailing
methods often rely on intermediate stages and lacking pixel‐level analysis. Moreover, their …

The traditional multi-person human pose estimation method has several problems including
low real-time detection effect, low recognition efficiency, and a large number of calculation …

Multi-human parsing aims to segment every body part of every human instance. Nearly all
state-of-the-art methods follow the “detection first” or “segmentation first” pipelines. Different …