Search Results for author:
Found 28 papers, 10 papers with code
Direct Dense Pose Estimation
In addition, DDP is computationally more efficient than previous dense pose estimation methods, and it reduces jitters when applied to a video sequence, which is a problem plaguing the previous methods.
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.
Neural Radiance Fields for Outdoor Scene Relighting
Photorealistic editing of outdoor scenes from photographs requires a profound understanding of the image formation process and an accurate estimation of the scene geometry, reflectance and illumination.
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.
StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis
We perform volume rendering only to produce a low-resolution feature map and progressively apply upsampling in 2D to address the first issue.
Neural Rays for Occlusion-aware Image-based Rendering
On such a 3D point, these generalization methods will include inconsistent image features from invisible views, which interfere with the radiance field construction.
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis.
NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction
In NeuS, we propose to represent a surface as the zero-level set of a signed distance function (SDF) and develop a new volume rendering method to train a neural SDF representation.
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.
Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods.
Real-time Deep Dynamic Characters
We propose a deep videorealistic 3D human character model displaying highly realistic shape, motion, and dynamic appearance learned in a new weakly supervised way from multi-view imagery.
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.
Efficient and Differentiable Shadow Computation for Inverse Problems
Differentiable rendering has received increasing interest for image-based inverse problems.
Adaptive Surface Normal Constraint for Depth Estimation
We present a novel method for single image depth estimation using surface normal constraints.
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.
Learning Speech-driven 3D Conversational Gestures from Video
We propose the first approach to automatically and jointly synthesize both the synchronous 3D conversational body and hand gestures, as well as 3D face and head animations, of a virtual character from speech input.
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.
Learnable Motion Coherence for Correspondence Pruning
We propose a novel formulation of fitting coherent motions with a smooth function on a graph of correspondences and show that this formulation allows a closed-form solution by graph Laplacian.
Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks
We present a novel method for multi-view depth estimation from a single video, which is a critical task in various applications, such as perception, reconstruction and robot navigation.
SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform
Segmenting arbitrary 3D objects into constituent parts that are structurally meaningful is a fundamental problem encountered in a wide range of computer graphics applications.
Neural Sparse Voxel Fields
We also demonstrate several challenging tasks, including multi-scene learning, free-viewpoint rendering of a moving human, and large-scale scene rendering.
Vid2Curve: Simultaneous Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera.
MulayCap: Multi-layer Human Performance Capture Using A Monocular Video Camera
We introduce MulayCap, a novel human performance capture method using a monocular video camera without the need for pre-scanning.
Occlusion-Aware Depth Estimation with Adaptive Normal Constraints
We present a new learning-based method for multi-frame depth estimation from a color video, which is a fundamental problem in scene understanding, robot navigation or handheld 3D reconstruction.
Unsupervised Learning of Intrinsic Structural Representation Points
The 3D structure points produced by our method encode the shape structure intrinsically and exhibit semantic consistency across all the shape instances with similar structures.
Neural Human Video Rendering by Learning Dynamic Textures and Rendering-to-Video Translation
In this paper, we propose a novel human video synthesis method that approaches these limiting factors by explicitly disentangling the learning of time-coherent fine-scale details from the embedding of the human in 2D screen space.
Neural Rendering and Reenactment of Human Actor Videos
In contrast to conventional human character rendering, we do not require the availability of a production-quality photo-realistic 3D model of the human, but instead rely on a video sequence in conjunction with a (medium-quality) controllable 3D template model of the person.
