Search Results for author:
Found 17 papers, 3 papers with code
Egocentric Whole-Body Motion Capture with FisheyeViT and Diffusion-Based Motion Refinement
In this work, we explore egocentric whole-body motion capture using a single fisheye camera, which simultaneously estimates human body and hand motion.
Ranked #1 on
Egocentric Pose Estimation
on GlobalEgoMocap Test Dataset
(using extra training data)
Preface: A Data-driven Volumetric Prior for Few-shot Ultra High-resolution Face Synthesis
NeRFs have enabled highly realistic synthesis of human faces including complex appearance and reflectance effects of hair and skin.
Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses.
Scene-aware Egocentric 3D Human Pose Estimation
To this end, we propose an egocentric depth estimation network to predict the scene depth map from a wide-view egocentric fisheye camera while mitigating the occlusion of the human body with a depth-inpainting network.
Ranked #3 on
Egocentric Pose Estimation
on GlobalEgoMocap Test Dataset
(using extra training data)
Estimating Egocentric 3D Human Pose in the Wild with External Weak Supervision
Specifically, we first generate pseudo labels for the EgoPW dataset with a spatio-temporal optimization method by incorporating the external-view supervision.
Ranked #4 on
Egocentric Pose Estimation
on GlobalEgoMocap Test Dataset
(using extra training data)
EgoRenderer: Rendering Human Avatars from Egocentric Camera Images
We next combine the target pose image and the textures into a combined feature image, which is transformed into the output color image using a neural image translation network.
Neural Actor: Neural Free-view Synthesis of Human Actors with Pose Control
To address this problem, we utilize a coarse body model as the proxy to unwarp the surrounding 3D space into a canonical pose.
Estimating Egocentric 3D Human Pose in Global Space
Furthermore, these methods suffer from limited accuracy and temporal instability due to ambiguities caused by the monocular setup and the severe occlusion in a strongly distorted egocentric perspective.
Ranked #4 on
Egocentric Pose Estimation
on SceneEgo
(using extra training data)
HumanGAN: A Generative Model of Humans Images
We address these limitations and present a generative model for images of dressed humans offering control over pose, local body part appearance and garment style.
Style and Pose Control for Image Synthesis of Humans from a Single Monocular View
Photo-realistic re-rendering of a human from a single image with explicit control over body pose, shape and appearance enables a wide range of applications, such as human appearance transfer, virtual try-on, motion imitation, and novel view synthesis.
Neural Re-Rendering of Humans from a Single Image
Human re-rendering from a single image is a starkly under-constrained problem, and state-of-the-art algorithms often exhibit undesired artefacts, such as over-smoothing, unrealistic distortions of the body parts and garments, or implausible changes of the texture.
Pose-Guided Human Animation from a Single Image in the Wild
We present a new pose transfer method for synthesizing a human animation from a single image of a person controlled by a sequence of body poses.
Object Detection and Recognition of Swap-Bodies using Camera mounted on a Vehicle
Collecting enough images for training the model is a critical step towards achieving good results.
Learning Quadrangulated Patches For 3D Shape Processing
We propose a system for surface completion and inpainting of 3D shapes using generative models, learnt on local patches.
Structured 2D Representation of 3D Data for Shape Processing
We represent 3D shape by structured 2D representations of fixed length making it feasible to apply well investigated 2D convolutional neural networks (CNN) for both discriminative and geometric tasks on 3D shapes.
Learning 3D Shapes as Multi-Layered Height-maps using 2D Convolutional Networks
We present a novel global representation of 3D shapes, suitable for the application of 2D CNNs.
Learning quadrangulated patches for 3D shape parameterization and completion
By encoding 3D surface detail on local patches, we learn a patch dictionary that identifies principal surface features of the shape.
