Search Results for author:
Found 19 papers, 14 papers with code
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling
We propose EMAGE, a framework to generate full-body human gestures from audio and masked gestures, encompassing facial, local body, hands, and global movements.
Ranked #1 on
3D Face Animation
on BEAT2
Skinned Motion Retargeting with Residual Perception of Motion Semantics & Geometry
Driven by our explored distance-based losses that explicitly model the motion semantics and geometry, these two modules can learn residual motion modifications on the source motion to generate plausible retargeted motion in a single inference without post-processing.
Audio2Gestures: Generating Diverse Gestures from Audio
Finally, we demonstrate that our method can be readily used to generate motion sequences with user-specified motion clips on the timeline.
Learning Audio-Driven Viseme Dynamics for 3D Face Animation
We show that the predicted viseme curves can be applied to different viseme-rigged characters to yield various personalized animations with realistic and natural facial motions.
CARD: Semantic Segmentation with Efficient Class-Aware Regularized Decoder
Extensive experiments and ablation studies conducted on multiple benchmark datasets demonstrate that the proposed CAR can boost the accuracy of all baseline models by up to 2. 23% mIOU with superior generalization ability.
NEURAL MARIONETTE: A Transformer-based Multi-action Human Motion Synthesis System
Along with the novel system, we also present a new dataset dedicated to the multi-action motion synthesis task, which contains both action tags and their contextual information.
Learning to Construct 3D Building Wireframes from 3D Line Clouds
The network takes a line cloud as input , i. e., a nonstructural and unordered set of 3D line segments extracted from multi-view images, and outputs a 3D wireframe of the underlying building, which consists of a sparse set of 3D junctions connected by line segments.
Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation
We propose WarpingGAN, an effective and efficient 3D point cloud generation network.
REALY: Rethinking the Evaluation of 3D Face Reconstruction
The evaluation of 3D face reconstruction results typically relies on a rigid shape alignment between the estimated 3D model and the ground-truth scan.
CAR: Class-aware Regularizations for Semantic Segmentation
Recent segmentation methods, such as OCR and CPNet, utilizing "class level" information in addition to pixel features, have achieved notable success for boosting the accuracy of existing network modules.
Ranked #8 on
Semantic Segmentation
on PASCAL Context
Audio2Gestures: Generating Diverse Gestures from Speech Audio with Conditional Variational Autoencoders
In order to overcome this problem, we propose a novel conditional variational autoencoder (VAE) that explicitly models one-to-many audio-to-motion mapping by splitting the cross-modal latent code into shared code and motion-specific code.
Ranked #3 on
Gesture Generation
on BEAT
Orthonormal Product Quantization Network for Scalable Face Image Retrieval
Experiments are conducted on four commonly-used face datasets under both seen and unseen identities retrieval settings.
Animatable Neural Radiance Fields from Monocular RGB Videos
We present animatable neural radiance fields (animatable NeRF) for detailed human avatar creation from monocular videos.
Model-based 3D Hand Reconstruction via Self-Supervised Learning
For the first time, we demonstrate the feasibility of training an accurate 3D hand reconstruction network without relying on manual annotations.
High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies
We present a fully automatic system that can produce high-fidelity, photo-realistic 3D digital human heads with a consumer RGB-D selfie camera.
Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval
Deep hashing models have been proposed as an efficient method for large-scale similarity search.
Deep Class-Wise Hashing: Semantics-Preserving Hashing via Class-wise Loss
In this regard, we propose a novel deep supervised hashing model to learn more compact class-level similarity preserving binary codes.
Directional Statistics-based Deep Metric Learning for Image Classification and Retrieval
Deep distance metric learning (DDML), which is proposed to learn image similarity metrics in an end-to-end manner based on the convolution neural network, has achieved encouraging results in many computer vision tasks.$L2$-normalization in the embedding space has been used to improve the performance of several DDML methods.
