Search Results for author:
Found 9 papers, 3 papers with code
Matrix3D: Large Photogrammetry Model All-in-One
We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model.
EmoTalk3D: High-Fidelity Free-View Synthesis of Emotional 3D Talking Head
We present a novel approach for synthesizing 3D talking heads with controllable emotion, featuring enhanced lip synchronization and rendering quality.
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians
Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation.
Direct2.5: Diverse Text-to-3D Generation via Multi-view 2.5D Diffusion
The multi-view 2. 5D diffusion directly models the structural distribution of 3D data, while still maintaining the strong generalization ability of the original 2D diffusion model, filling the gap between 2D diffusion-based and direct 3D diffusion-based methods for 3D content generation.
JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling
We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e. g., depth maps).
AvatarBooth: High-Quality and Customizable 3D Human Avatar Generation
Unlike previous approaches that can only synthesize avatars based on simple text descriptions, our method enables the creation of personalized avatars from casually captured face or body images, while still supporting text-based model generation and editing.
High-Fidelity 3D Face Generation from Natural Language Descriptions
Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence.
RAFaRe: Learning Robust and Accurate Non-parametric 3D Face Reconstruction from Pseudo 2D&3D Pairs
We propose a robust and accurate non-parametric method for single-view 3D face reconstruction (SVFR).
Live Speech Portraits: Real-Time Photorealistic Talking-Head Animation
The first stage is a deep neural network that extracts deep audio features along with a manifold projection to project the features to the target person's speech space.
