Search Results for author:
Found 17 papers, 8 papers with code
MyPortrait: Morphable Prior-Guided Personalized Portrait Generation
We incorporate personalized prior in a monocular video and morphable prior in 3D face morphable space for generating personalized details under novel controllable parameters.
Probabilistic Triangulation for Uncalibrated Multi-View 3D Human Pose Estimation
The key idea is to use a probability distribution to model the camera pose and iteratively update the distribution from 2D features instead of using camera pose.
AttT2M: Text-Driven Human Motion Generation with Multi-Perspective Attention Mechanism
The latter is from the cross-modal perspective, which is used to learn the sentence-level and word-level motion-text cross-modal relationship.
Ranked #10 on
Motion Synthesis
on HumanML3D
Unpaired Multi-domain Attribute Translation of 3D Facial Shapes with a Square and Symmetric Geometric Map
We propose a learning framework for 3D facial attribute translation to relieve these limitations.
Pose-aware Attention Network for Flexible Motion Retargeting by Body Part
Moreover, we also show that our framework can generate reasonable results even for a more challenging retargeting scenario, like retargeting between bipedal and quadrupedal skeletons because of the body part retargeting strategy and PAN.
Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction
However, considering the variants and diverse action types in human motion data, the cross-dependency of the spatio-temporal relationships will be difficult to depict due to the decoupled modeling strategy, which may also exacerbate the problem of insufficient generalization.
Towards Fine-grained 3D Face Dense Registration: An Optimal Dividing and Diffusing Method
This method is then extended to 3D surface by formulating a local registration problem for dividing and a linear least-square problem for diffusing, with constraints on fixed features.
Sequential 3D Human Pose Estimation Using Adaptive Point Cloud Sampling Strategy
In this paper, we present a new perspective on the 3D human pose estimation method from point cloud sequences.
Ranked #1 on
Pose Estimation
on ITOP front-view
3D-TalkEmo: Learning to Synthesize 3D Emotional Talking Head
Impressive progress has been made in audio-driven 3D facial animation recently, but synthesizing 3D talking-head with rich emotion is still unsolved.
Multiscale Mesh Deformation Component Analysis with Attention-based Autoencoders
The attention mechanism is designed to learn to softly weight multi-scale deformation components in active deformation regions, and the stacked attention-based autoencoder is learned to represent the deformation components at different scales.
A Backbone Replaceable Fine-tuning Framework for Stable Face Alignment
The proposed framework achieves at least 40% improvement on stability evaluation metrics while enhancing detection accuracy versus state-of-the-art methods.
Deep Generation of Face Images from Sketches
Our method essentially uses input sketches as soft constraints and is thus able to produce high-quality face images even from rough and/or incomplete sketches.
Realtime and Accurate 3D Eye Gaze Capture with DCNN-based Iris and Pupil Segmentation
A comparison against Wang et al.[3] shows that our method advances the state of the art in 3D eye tracking using a single RGB camera.
Learning Bidirectional LSTM Networks for Synthesizing 3D Mesh Animation Sequences
In this paper, we present a novel method for learning to synthesize 3D mesh animation sequences with long short-term memory (LSTM) blocks and mesh-based convolutional neural networks (CNNs).
Graphics
Variational Autoencoders for Deforming 3D Mesh Models
3D geometric contents are becoming increasingly popular.
Graphics
Mesh-based Autoencoders for Localized Deformation Component Analysis
Spatially localized deformation components are very useful for shape analysis and synthesis in 3D geometry processing.
Graphics
Sparse Data Driven Mesh Deformation
This along with a suitably chosen deformation basis including spatially localized deformation modes leads to significant advantages, including more meaningful, reliable, and efficient deformations because fewer and localized deformation modes are applied.
Graphics
