The objective of human pose estimation (HPE) derived from deep learning aims to
accurately estimate and predict the human body posture in images or videos via the …

This work proposes a unified framework called UniPose to detect keypoints of any
articulated (eg, human and animal), rigid, and soft objects via visual or textual prompts for …

In this paper we focus on capturing closely interacted two-person motions from monocular
videos an important yet understudied topic. Unlike less-interacted motions closely interacted …

Human pose analysis has garnered significant attention within both the research community
and practical applications, owing to its expanding array of uses, including gaming, video …

Human pose forecasting garners attention for its diverse applications. However challenges
in modeling the multi-modal nature of human motion and intricate interactions among agents …

This paper presents the DiffusionRegPose a novel approach to multi-person pose
estimation that converts a one-stage end-to-end keypoint regression model into a diffusion …

Accurate 3D lane estimation is crucial for ensuring safety in autonomous driving. However,
prevailing monocular techniques suffer from depth loss and lighting variations, hampering …

Tracking the articulated poses of multiple individuals in complex videos is a highly
challenging task due to a variety of factors that compromise the accuracy of estimation and …

Multi-person pose estimation (MPPE) presents a challenging yet crucial task in computer
vision. Most existing methods predominantly concentrate on isolated interaction either …

We present VisionLLM v2, an end-to-end generalist multimodal large model (MLLM) that
unifies visual perception, understanding, and generation within a single framework. Unlike …