Search Results for author:
Found 12 papers, 5 papers with code
Convolutional Pose Machines
Pose Machines provide a sequential prediction framework for learning rich implicit spatial models.
Ranked #2 on
Classification
on RSSCN7
Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
We present an approach to efficiently detect the 2D pose of multiple people in an image.
Ranked #6 on
Keypoint Detection
on MPII Multi-Person
Supervision-by-Registration: An Unsupervised Approach to Improve the Precision of Facial Landmark Detectors
In this paper, we present supervision-by-registration, an unsupervised approach to improve the precision of facial landmark detectors on both images and video.
Ranked #1 on
Facial Landmark Detection
on 300-VW (C)
OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields
OpenPose: Real-time multi-person keypoint detection library for body, face, hands, and foot estimation
Ranked #4 on
Pose Estimation
on MPII Single Person
Image Disentanglement and Uncooperative Re-Entanglement for High-Fidelity Image-to-Image Translation
Cross-domain image-to-image translation should satisfy two requirements: (1) preserve the information that is common to both domains, and (2) generate convincing images covering variations that appear in the target domain.
Supervision by Registration and Triangulation for Landmark Detection
End-to-end training is made possible by differentiable registration and 3D triangulation modules.
SimPoE: Simulated Character Control for 3D Human Pose Estimation
Based on this refined kinematic pose, the policy learns to compute dynamics-based control (e. g., joint torques) of the character to advance the current-frame pose estimate to the pose estimate of the next frame.
Ranked #229 on
3D Human Pose Estimation
on Human3.6M
Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality
Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR).
Driving-Signal Aware Full-Body Avatars
The core intuition behind our method is that better drivability and generalization can be achieved by disentangling the driving signals and remaining generative factors, which are not available during animation.
LiP-Flow: Learning Inference-time Priors for Codec Avatars via Normalizing Flows in Latent Space
To mitigate this asymmetry, we introduce a prior model that is conditioned on the runtime inputs and tie this prior space to the 3D face model via a normalizing flow in the latent space.
Drivable Volumetric Avatars using Texel-Aligned Features
To circumvent this, we propose a novel volumetric avatar representation by extending mixtures of volumetric primitives to articulated objects.
Fast Registration of Photorealistic Avatars for VR Facial Animation
In this work, we first show that the domain gap between the avatar and headset-camera images is one of the primary sources of difficulty, where a transformer-based architecture achieves high accuracy on domain-consistent data, but degrades when the domain-gap is re-introduced.
