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Found 26 papers, 16 papers with code
MagicDance: Realistic Human Dance Video Generation with Motions & Facial Expressions Transfer
In this work, we propose MagicDance, a diffusion-based model for 2D human motion and facial expression transfer on challenging human dance videos.
Consistent-1-to-3: Consistent Image to 3D View Synthesis via Geometry-aware Diffusion Models
Specifically, we decompose the NVS task into two stages: (i) transforming observed regions to a novel view, and (ii) hallucinating unseen regions.
MVDream: Multi-view Diffusion for 3D Generation
We introduce MVDream, a multi-view diffusion model that is able to generate consistent multi-view images from a given text prompt.
OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis
We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses.
PAniC-3D: Stylized Single-view 3D Reconstruction from Portraits of Anime Characters
We propose PAniC-3D, a system to reconstruct stylized 3D character heads directly from illustrated (p)ortraits of (ani)me (c)haracters.
AgileGAN3D: Few-Shot 3D Portrait Stylization by Augmented Transfer Learning
Capitalizing on the recent advancement of 3D-aware GAN models, we perform \emph{guided transfer learning} on a pretrained 3D GAN generator to produce multi-view-consistent stylized renderings.
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360$^{\circ}$
We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in $360^\circ$ with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
PanoHead: Geometry-Aware 3D Full-Head Synthesis in 360deg
We propose PanoHead, the first 3D-aware generative model that enables high-quality view-consistent image synthesis of full heads in 360deg with diverse appearance and detailed geometry using only in-the-wild unstructured images for training.
AvatarGen: A 3D Generative Model for Animatable Human Avatars
Specifically, we decompose the generative 3D human synthesis into pose-guided mapping and canonical representation with predefined human pose and shape, such that the canonical representation can be explicitly driven to different poses and shapes with the guidance of a 3D parametric human model SMPL.
AvatarGen: a 3D Generative Model for Animatable Human Avatars
Unsupervised generation of clothed virtual humans with various appearance and animatable poses is important for creating 3D human avatars and other AR/VR applications.
Handling Data Heterogeneity in Federated Learning via Knowledge Distillation and Fusion
The key idea in FedKF is to let the server return the global knowledge to be fused with the local knowledge in each training round so that the local model can be regularized towards the global optima.
IDE-3D: Interactive Disentangled Editing for High-Resolution 3D-aware Portrait Synthesis
Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility.
SemanticStyleGAN: Learning Compositional Generative Priors for Controllable Image Synthesis and Editing
When combined with editing methods designed for StyleGANs, it can achieve a more fine-grained control to edit synthesized or real images.
Federated Learning with Domain Generalization
Federated Learning (FL) enables a group of clients to jointly train a machine learning model with the help of a centralized server.
Semantic StyleGAN
SemanticStyleGAN presents a method where a generator is trained to model local semantic parts separately and synthesizes images in a compositional way.
Lifting 2D StyleGAN for 3D-Aware Face Generation
We propose a framework, called LiftedGAN, that disentangles and lifts a pre-trained StyleGAN2 for 3D-aware face generation.
Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data
In recent years, significant progress has been made in face recognition, which can be partially attributed to the availability of large-scale labeled face datasets.
Towards Universal Representation Learning for Deep Face Recognition
Recognizing wild faces is extremely hard as they appear with all kinds of variations.
Recurrent Embedding Aggregation Network for Video Face Recognition
Recurrent networks have been successful in analyzing temporal data and have been widely used for video analysis.
Probabilistic Face Embeddings
Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space.
Ranked #1 on
Face Verification
on IJB-C
(training dataset metric)
Video Face Recognition: Component-wise Feature Aggregation Network (C-FAN)
We propose a new approach to video face recognition.
WarpGAN: Automatic Caricature Generation
We propose, WarpGAN, a fully automatic network that can generate caricatures given an input face photo.
DocFace+: ID Document to Selfie Matching
Numerous activities in our daily life require us to verify who we are by showing our ID documents containing face images, such as passports and driver licenses, to human operators.
DocFace: Matching ID Document Photos to Selfies
Numerous activities in our daily life, including transactions, access to services and transportation, require us to verify who we are by showing our ID documents containing face images, e. g. passports and driver licenses.
Face Recognition: Primates in the Wild
We present a new method of primate face recognition, and evaluate this method on several endangered primates, including golden monkeys, lemurs, and chimpanzees.
Face Clustering: Representation and Pairwise Constraints
Given this representation, we design a clustering algorithm, Conditional Pairwise Clustering (ConPaC), which directly estimates the adjacency matrix only based on the similarity between face images.
