Abstract 3D human pose data collected in controlled laboratory settings present challenges
for pose estimators that generalize across diverse scenarios. To address this domain …

Diffusion models represent a promising avenue for image generation, having demonstrated
competitive performance in pose-guided person image generation. However, existing …

Modern multi-layer perceptron (MLP) models have shown competitive results in learning
visual representations without self-attention. However, existing MLP models are not good at …

The estimation of 3D human poses from monocular videos presents a significant challenge.
The existing methods face the problems of deep ambiguity and self-occlusion. To overcome …

Transformers have been successfully applied in the field of video-based 3D human pose
estimation. However the high computational costs of these video pose transformers (VPTs) …

Traditional methods for human localization and pose estimation (HPE), which mainly rely on
RGB images as an input modality, confront substantial limitations in real-world applications …

Accurately estimating the 3D pose of humans in video sequences requires both accuracy
and a well-structured architecture. With the success of transformers, we introduce the …

In the field of multi-object tracking (MOT), traditional methods often rely on the Kalman Filter
for motion prediction, leveraging its strengths in linear motion scenarios. However, the …

Human motion prediction is crucial for human-centric multimedia understanding and
interacting. Current methods typically rely on ground truth human poses as observed input …

Recently there were remarkable advances in image editing tasks in various ways.
Nevertheless existing image editing models are not designed for Human-Object Interaction …