Search Results for author:
Found 11 papers, 3 papers with code
Jump Cut Smoothing for Talking Heads
We leverage the appearance of the subject from the other source frames in the video, fusing it with a mid-level representation driven by DensePose keypoints and face landmarks.
Generative Powers of Ten
We present a method that uses a text-to-image model to generate consistent content across multiple image scales, enabling extreme semantic zooms into a scene, e. g., ranging from a wide-angle landscape view of a forest to a macro shot of an insect sitting on one of the tree branches.
QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query
The existing end-to-end methods rely on dense representations to preserve the spatial detail and structure for precise keypoint localization.
AdaptivePose++: A Powerful Single-Stage Network for Multi-Person Pose Regression
With the proposed body representation, we further deliver a compact single-stage multi-person pose regression network, termed as AdaptivePose.
Learning Quality-aware Representation for Multi-person Pose Regression
Off-the-shelf single-stage multi-person pose regression methods generally leverage the instance score (i. e., confidence of the instance localization) to indicate the pose quality for selecting the pose candidates.
Single-Stage Is Enough: Multi-Person Absolute 3D Pose Estimation
The existing multi-person absolute 3D pose estimation methods are mainly based on two-stage paradigm, i. e., top-down or bottom-up, leading to redundant pipelines with high computation cost.
AdaptivePose: Human Parts as Adaptive Points
Multi-person pose estimation methods generally follow top-down and bottom-up paradigms, both of which can be considered as two-stage approaches thus leading to the high computation cost and low efficiency.
The 2nd Anti-UAV Workshop & Challenge: Methods and Results
The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking.
SPCNet:Spatial Preserve and Content-aware Network for Human Pose Estimation
To alleviate these issues, we propose a novel Spatial Preserve and Content-aware Network(SPCNet), which includes two effective modules: Dilated Hourglass Module(DHM) and Selective Information Module(SIM).
Supervised Quantization for Similarity Search
In this paper, we address the problem of searching for semantically similar images from a large database.
Multi-Scale Learning for Low-Resolution Person Re-Identification
In real world person re-identification (re-id), images of people captured at very different resolutions from different locations need be matched.
