Search Results for author:
Found 26 papers, 12 papers with code
Towards Variable and Coordinated Holistic Co-Speech Motion Generation
This paper addresses the problem of generating lifelike holistic co-speech motions for 3D avatars, focusing on two key aspects: variability and coordination.
Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization
We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT).
Pairwise Similarity Learning is SimPLE
In this paper, we focus on a general yet important learning problem, pairwise similarity learning (PSL).
Text-Guided Generation and Editing of Compositional 3D Avatars
We argue that existing methods are limited because they employ a monolithic modeling approach, using a single representation for the head, face, hair, and accessories.
Rethinking Voice-Face Correlation: A Geometry View
Previous works on voice-face matching and voice-guided face synthesis demonstrate strong correlations between voice and face, but mainly rely on coarse semantic cues such as gender, age, and emotion.
The Hidden Dance of Phonemes and Visage: Unveiling the Enigmatic Link between Phonemes and Facial Features
This work unveils the enigmatic link between phonemes and facial features.
Emotional Speech-Driven Animation with Content-Emotion Disentanglement
While the best recent methods generate 3D animations that are synchronized with the input audio, they largely ignore the impact of emotions on facial expressions.
Generating Holistic 3D Human Motion from Speech
This work addresses the problem of generating 3D holistic body motions from human speech.
Ranked #3 on
Gesture Generation
on BEAT2
Self-Supervised 3D Face Reconstruction via Conditional Estimation
We present a conditional estimation (CEST) framework to learn 3D facial parameters from 2D single-view images by self-supervised training from videos.
Ranked #16 on
3D Face Reconstruction
on REALY
SphereFace Revived: Unifying Hyperspherical Face Recognition
As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin.
SphereFace2: Binary Classification is All You Need for Deep Face Recognition
In this paper, we start by identifying the discrepancy between training and evaluation in the existing multi-class classification framework and then discuss the potential limitations caused by the "competitive" nature of softmax normalization.
MeshTalk: 3D Face Animation from Speech using Cross-Modality Disentanglement
To improve upon existing models, we propose a generic audio-driven facial animation approach that achieves highly realistic motion synthesis results for the entire face.
Ranked #2 on
3D Face Animation
on VOCASET
Face Reconstruction from Voice using Generative Adversarial Networks
The network learns to generate faces from voices by matching the identities of generated faces to those of the speakers, on a training set.
Reconstructing faces from voices
Voice profiling aims at inferring various human parameters from their speech, e. g. gender, age, etc.
Disjoint Mapping Network for Cross-modal Matching of Voices and Faces
We propose a novel framework, called Disjoint Mapping Network (DIMNet), for cross-modal biometric matching, in particular of voices and faces.
Optimal Strategies for Matching and Retrieval Problems by Comparing Covariates
In many retrieval problems, where we must retrieve one or more entries from a gallery in response to a probe, it is common practice to learn to do by directly comparing the probe and gallery entries to one another.
Range Loss for Deep Face Recognition With Long-Tailed Training Data
Unlike these work, this paper investigated how long-tailed data impact the training of face CNNs and develop a novel loss function, called range loss, to effectively utilize the tailed data in training process.
SphereFace: Deep Hypersphere Embedding for Face Recognition
This paper addresses deep face recognition (FR) problem under open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space.
Ranked #1 on
Face Verification
on CK+
Large-Margin Softmax Loss for Convolutional Neural Networks
Cross-entropy loss together with softmax is arguably one of the most common used supervision components in convolutional neural networks (CNNs).
Range Loss for Deep Face Recognition with Long-tail
Convolutional neural networks have achieved great improvement on face recognition in recent years because of its extraordinary ability in learning discriminative features of people with different identities.
A Discriminative Feature Learning Approach for Deep Face Recognition
In most of the available CNNs, the softmax loss function is used as the supervision signal to train the deep model.
Latent Factor Guided Convolutional Neural Networks for Age-Invariant Face Recognition
In order to address this problem, we propose a novel deep face recognition framework to learn the age-invariant deep face features through a carefully designed CNN model.
Ranked #7 on
Age-Invariant Face Recognition
on CACDVS
Jointly Learning Non-negative Projection and Dictionary with Discriminative Graph Constraints for Classification
Sparse coding with dictionary learning (DL) has shown excellent classification performance.
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
Practical face recognition has been studied in the past decades, but still remains an open challenge.
Structured Occlusion Coding for Robust Face Recognition
We propose the structured occlusion coding (SOC) to address occlusion problems.
KCRC-LCD: Discriminative Kernel Collaborative Representation with Locality Constrained Dictionary for Visual Categorization
The LCD similarity measure can be kernelized under KCRC, which theoretically links CRC and LCD under the kernel method.
