Search Results for author:
Found 19 papers, 6 papers with code
SphereHead: Stable 3D Full-head Synthesis with Spherical Tri-plane Representation
We further introduce a view-image consistency loss for the discriminator to emphasize the correspondence of the camera parameters and the images.
Aerial Lifting: Neural Urban Semantic and Building Instance Lifting from Aerial Imagery
We then introduce a novel cross-view instance label grouping strategy based on the 3D scene representation to mitigate the multi-view inconsistency problem in the 2D instance labels.
OMG: Occlusion-friendly Personalized Multi-concept Generation in Diffusion Models
We also observe that the initiation denoising timestep for noise blending is the key to identity preservation and layout.
MOC-RVQ: Multilevel Codebook-assisted Digital Generative Semantic Communication
Vector quantization-based image semantic communication systems have successfully boosted transmission efficiency, but face a challenge with conflicting requirements between codebook design and digital constellation modulation.
RichDreamer: A Generalizable Normal-Depth Diffusion Model for Detail Richness in Text-to-3D
Lifting 2D diffusion for 3D generation is a challenging problem due to the lack of geometric prior and the complex entanglement of materials and lighting in natural images.
Knowledge Base Enabled Semantic Communication: A Generative Perspective
Semantic communication is widely touted as a key technology for propelling the sixth-generation (6G) wireless networks.
Forward Flow for Novel View Synthesis of Dynamic Scenes
This paper proposes a neural radiance field (NeRF) approach for novel view synthesis of dynamic scenes using forward warping.
REC-MV: REconstructing 3D Dynamic Cloth from Monocular Videos
To address the above limitations, in this paper, we formulate this task as an optimization problem of 3D garment feature curves and surface reconstruction from monocular video.
MVImgNet: A Large-scale Dataset of Multi-view Images
The birth of ImageNet drives a remarkable trend of "learning from large-scale data" in computer vision.
Efficient Large-scale Scene Representation with a Hybrid of High-resolution Grid and Plane Features
Based on this hybrid representation, we propose a fast optimization NeRF variant, called GP-NeRF, that achieves better rendering results while maintaining a compact model size.
NeuralMPS: Non-Lambertian Multispectral Photometric Stereo via Spectral Reflectance Decomposition
In this paper, we propose a deep neural network named NeuralMPS to solve the MPS problem under general non-Lambertian spectral reflectances.
S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint
Different from existing single-view methods which can only recover a 2. 5D scene representation (i. e., a normal / depth map for the visible surface), our method learns a neural reflectance field to represent the 3D geometry and BRDFs of a scene.
PS-NeRF: Neural Inverse Rendering for Multi-view Photometric Stereo
It then jointly optimizes the surface normals, spatially-varying BRDFs, and lights based on a shadow-aware differentiable rendering layer.
Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes
We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images.
Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis
The second module contains a density and a color grid to model the geometry and density of the scene.
JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction
In this paper, we focus on improving the quality of face in the reconstruction and propose a novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of the implicit function based approach and model based approach.
ETHSeg: An Amodel Instance Segmentation Network and a Real-World Dataset for X-Ray Waste Inspection
Inspired by the fact that X-ray has a strong penetrating power to see through the bag and overlapping objects, we propose to perform waste inspection efficiently using X-ray images without the need to open the bag.
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset
Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction.
