Search Results for author:
Found 9 papers, 5 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.
Dense Keypoints via Multiview Supervision
(2) Geometric consistency: every point in the continuous correspondence fields must satisfy the multiview consistency collectively.
On the Use and Misuse of Absorbing States in Multi-agent Reinforcement Learning
In this work, we first demonstrate that sample complexity increases with the quantity of absorbing states in a toy supervised learning task for a fully connected network, while attention is more robust to variable size input.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Semi-supervised Dense Keypoints Using Unlabeled Multiview Images
(2) Geometric consistency: every point in the continuous correspondence fields must satisfy the multiview consistency collectively.
Unity Perception: Generate Synthetic Data for Computer Vision
We introduce the Unity Perception package which aims to simplify and accelerate the process of generating synthetic datasets for computer vision tasks by offering an easy-to-use and highly customizable toolset.
Technology Readiness Levels for Machine Learning Systems
The development and deployment of machine learning (ML) systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end.
Where Will They Go? Predicting Fine-Grained Adversarial Multi-Agent Motion using Conditional Variational Autoencoders
Simultaneously and accurately forecasting the behavior of many interacting agents is imperative for computer vision applications to be widely deployed (e. g., autonomous vehicles, security, surveillance, sports).
