Search Results for author:
Found 19 papers, 10 papers with code
DreamActor-H1: High-Fidelity Human-Product Demonstration Video Generation via Motion-designed Diffusion Transformers
Trained on a hybrid dataset with extensive data augmentation strategies, our approach outperforms state-of-the-art techniques in maintaining the identity integrity of both humans and products and generating realistic demonstration motions.
DreamActor-M1: Holistic, Expressive and Robust Human Image Animation with Hybrid Guidance
For motion guidance, our hybrid control signals that integrate implicit facial representations, 3D head spheres, and 3D body skeletons achieve robust control of facial expressions and body movements, while producing expressive and identity-preserving animations.
Stereo-Talker: Audio-driven 3D Human Synthesis with Prior-Guided Mixture-of-Experts
This paper introduces Stereo-Talker, a novel one-shot audio-driven human video synthesis system that generates 3D talking videos with precise lip synchronization, expressive body gestures, temporally consistent photo-realistic quality, and continuous viewpoint control.
RAM-Avatar: Real-time Photo-Realistic Avatar from Monocular Videos with Full-body Control
This paper focuses on advancing the applicability of human avatar learning methods by proposing RAM-Avatar which learns a Real-time photo-realistic Avatar that supports full-body control from Monocular videos.
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.
GMTalker: Gaussian Mixture-based Audio-Driven Emotional Talking Video Portraits
Finally, we propose a personalized emotion-guided head generator with an emotion mapping network that can synthesize high-fidelity and faithful emotional video portraits.
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.
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 and Relightable Gaussians for High-fidelity Human Avatar Modeling
To associate 3D Gaussians with the animatable avatar, we learn a parametric template from the input videos, and then parameterize the template on two front & back canonical Gaussian maps where each pixel represents a 3D Gaussian.
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.
