Search Results for author:
Found 6 papers, 2 papers with code
3D Human Reconstruction in the Wild with Synthetic Data Using Generative Models
In this work, we show that synthetic data created by generative models is complementary to computer graphics (CG) rendered data for achieving remarkable generalization performance on diverse real-world scenes for 3D human pose and shape estimation (HPS).
Diffusion Models Trained with Large Data Are Transferable Visual Models
We show that, simply initializing image understanding models using a pre-trained UNet (or transformer) of diffusion models, it is possible to achieve remarkable transferable performance on fundamental vision perception tasks using a moderate amount of target data (even synthetic data only), including monocular depth, surface normal, image segmentation, matting, human pose estimation, among virtually many others.
Zolly: Zoom Focal Length Correctly for Perspective-Distorted Human Mesh Reconstruction
As it is hard to calibrate single-view RGB images in the wild, existing 3D human mesh reconstruction (3DHMR) methods either use a constant large focal length or estimate one based on the background environment context, which can not tackle the problem of the torso, limb, hand or face distortion caused by perspective camera projection when the camera is close to the human body.
Ranked #5 on
3D Human Pose Estimation
on 3DPW
Point-Teaching: Weakly Semi-Supervised Object Detection with Point Annotations
Point annotations are considerably more time-efficient than bounding box annotations.
Poseur: Direct Human Pose Regression with Transformers
We propose a direct, regression-based approach to 2D human pose estimation from single images.
Ranked #2 on
Keypoint Detection
on MS COCO
TFPose: Direct Human Pose Estimation with Transformers
We propose a human pose estimation framework that solves the task in the regression-based fashion.
Ranked #26 on
Pose Estimation
on MPII Human Pose
(using extra training data)
