Search Results for author:
Found 4 papers, 3 papers with code
AnthroNet: Conditional Generation of Humans via Anthropometrics
We present a novel human body model formulated by an extensive set of anthropocentric measurements, which is capable of generating a wide range of human body shapes and poses.
3D human pose and shape estimation
3D Human Reconstruction
+3
PSP-HDRI$+$: A Synthetic Dataset Generator for Pre-Training of Human-Centric Computer Vision Models
We introduce a new synthetic data generator PSP-HDRI$+$ that proves to be a superior pre-training alternative to ImageNet and other large-scale synthetic data counterparts.
PeopleSansPeople: A Synthetic Data Generator for Human-Centric Computer Vision
We found that pre-training a network using synthetic data and fine-tuning on various sizes of real-world data resulted in a keypoint AP increase of $+38. 03$ ($44. 43 \pm 0. 17$ vs. $6. 40$) for few-shot transfer (limited subsets of COCO-person train [2]), and an increase of $+1. 47$ ($63. 47 \pm 0. 19$ vs. $62. 00$) for abundant real data regimes, outperforming models trained with the same real data alone.
Approximated Robust Principal Component Analysis for Improved General Scene Background Subtraction
This article addresses a few critical issues including: embedding global motion parameters in the matrix decomposition model, i. e., estimation of global motion parameters simultaneously with the foreground/background separation task, considering matrix block-sparsity rather than generic matrix sparsity as natural feature in video processing applications, attenuating background ghosting effects when foreground is subtracted, and more critically providing an extremely efficient algorithm to solve the low-rank/sparse matrix decomposition task.
