Search Results for author:
Found 23 papers, 7 papers with code
Online Neural Path Guiding with Normalized Anisotropic Spherical Gaussians
The variance reduction speed of physically-based rendering is heavily affected by the adopted importance sampling technique.
NeuralUDF: Learning Unsigned Distance Fields for Multi-view Reconstruction of Surfaces with Arbitrary Topologies
In this paper, we propose to represent surfaces as the Unsigned Distance Function (UDF) and develop a new volume rendering scheme to learn the neural UDF representation.
TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer
In this paper, we introduce a set of simple yet effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images.
Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB Videos
Understanding dynamic hand motions and actions from egocentric RGB videos is a fundamental yet challenging task due to self-occlusion and ambiguity.
Progressively-connected Light Field Network for Efficient View Synthesis
ProLiF encodes a 4D light field, which allows rendering a large batch of rays in one training step for image- or patch-level losses.
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors
The key idea of NeuRIS is to integrate estimated normal of indoor scenes as a prior in a neural rendering framework for reconstructing large texture-less shapes and, importantly, to do this in an adaptive manner to also enable the reconstruction of irregular shapes with fine details.
Learn to Predict How Humans Manipulate Large-sized Objects from Interactive Motions
We also observe that an object's intrinsic physical properties are useful for the object motion prediction, and thus design a set of object dynamic descriptors to encode such intrinsic properties.
SparseNeuS: Fast Generalizable Neural Surface Reconstruction from Sparse Views
We introduce SparseNeuS, a novel neural rendering based method for the task of surface reconstruction from multi-view images.
Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images
In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D.
Real-Time Style Modelling of Human Locomotion via Feature-Wise Transformations and Local Motion Phases
In this work we present a style modelling system that uses an animation synthesis network to model motion content based on local motion phases.
FaceFormer: Speech-Driven 3D Facial Animation with Transformers
Speech-driven 3D facial animation is challenging due to the complex geometry of human faces and the limited availability of 3D audio-visual data.
Joint Audio-Text Model for Expressive Speech-Driven 3D Facial Animation
The existing datasets are collected to cover as many different phonemes as possible instead of sentences, thus limiting the capability of the audio-based model to learn more diverse contexts.
DISP6D: Disentangled Implicit Shape and Pose Learning for Scalable 6D Pose Estimation
Scalable 6D pose estimation for rigid objects from RGB images aims at handling multiple objects and generalizing to novel objects.
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.
MotioNet: 3D Human Motion Reconstruction from Monocular Video with Skeleton Consistency
We introduce MotioNet, a deep neural network that directly reconstructs the motion of a 3D human skeleton from monocular video. While previous methods rely on either rigging or inverse kinematics (IK) to associate a consistent skeleton with temporally coherent joint rotations, our method is the first data-driven approach that directly outputs a kinematic skeleton, which is a complete, commonly used, motion representation.
Local motion phases for learning multi-contact character movements
Training a bipedal character to play basketball and interact with objects, or a quadruped character to move in various locomotion modes, are difficult tasks due to the fast and complex contacts happening during the motion.
Learning Whole-body Motor Skills for Humanoids
This paper presents a hierarchical framework for Deep Reinforcement Learning that acquires motor skills for a variety of push recovery and balancing behaviors, i. e., ankle, hip, foot tilting, and stepping strategies.
A new method to evaluate the dynamic air gap thickness and garment sliding of virtual clothes during walking
However, the virtual clothing fit evaluation is still under-researched.
Personalized 3D mannequin reconstruction based on 3D scanning
A personalized mannequin is essential for apparel customization using CAD technologies.
3D textile reconstruction based on KinectFusion and synthesized texture
Purpose The purpose of this paper is to present a novel framework of reconstructing the 3D textile model with synthesized texture.
Scanning and animating characters dressed in multiple-layer garments
In this paper, we propose a novel scanning-based solution for modeling and animating characters wearing multiple layers of clothes.
