Search Results for author:
Found 15 papers, 9 papers with code
GMTalker: Gaussian Mixture based Emotional talking video Portraits
Specifically, we propose a Gaussian Mixture based Expression Generator (GMEG) which can construct a continuous and multi-modal latent space, achieving more flexible emotion manipulation.
MonoGaussianAvatar: Monocular Gaussian Point-based Head Avatar
In response to these challenges, we propose MonoGaussianAvatar (Monocular Gaussian Point-based Head Avatar), a novel approach that harnesses 3D Gaussian point representation coupled with a Gaussian deformation field to learn explicit head avatars from monocular portrait videos.
Gaussian Head Avatar: Ultra High-fidelity Head Avatar via Dynamic Gaussians
Creating high-fidelity 3D head avatars has always been a research hotspot, but there remains a great challenge under lightweight sparse view setups.
InvertAvatar: Incremental GAN Inversion for Generalized Head Avatars
While high fidelity and efficiency are central to the creation of digital head avatars, recent methods relying on 2D or 3D generative models often experience limitations such as shape distortion, expression inaccuracy, and identity flickering.
Animatable Gaussians: Learning Pose-dependent Gaussian Maps for High-fidelity Human Avatar Modeling
Overall, our method can create lifelike avatars with dynamic, realistic and generalized appearances.
DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
The score distillation from this 3D-aware diffusion prior provides view-consistent guidance for the scene.
HAvatar: High-fidelity Head Avatar via Facial Model Conditioned Neural Radiance Field
The problem of modeling an animatable 3D human head avatar under light-weight setups is of significant importance but has not been well solved.
LatentAvatar: Learning Latent Expression Code for Expressive Neural Head Avatar
Existing approaches to animatable NeRF-based head avatars are either built upon face templates or use the expression coefficients of templates as the driving signal.
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single Video
Results and experiments demonstrate the superiority of our method in terms of image quality, full portrait video generation, and real-time re-animation compared to existing facial reenactment methods.
AvatarMAV: Fast 3D Head Avatar Reconstruction Using Motion-Aware Neural Voxels
AvatarMAV is the first to model both the canonical appearance and the decoupled expression motion by neural voxels for head avatar.
Next3D: Generative Neural Texture Rasterization for 3D-Aware Head Avatars
We propose a novel 3D GAN framework for unsupervised learning of generative, high-quality and 3D-consistent facial avatars from unstructured 2D images.
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.
FaceVerse: a Fine-grained and Detail-controllable 3D Face Morphable Model from a Hybrid Dataset
In the coarse module, we generate a base parametric model from large-scale RGB-D images, which is able to predict accurate rough 3D face models in different genders, ages, etc.
NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image
We propose NormalGAN, a fast adversarial learning-based method to reconstruct the complete and detailed 3D human from a single RGB-D image.
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs
Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest Iphone X.
