Search Results for author:
Found 12 papers, 7 papers with code
StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D
Our analysis identifies the core of these challenges as the interaction among noise levels in the 2D diffusion process, the architecture of the diffusion network, and the 3D model representation.
Pseudo-Generalized Dynamic View Synthesis from a Video
In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.
AutoFocusFormer: Image Segmentation off the Grid
To achieve this, we propose AutoFocusFormer (AFF), a local-attention transformer image recognition backbone, which performs adaptive downsampling by learning to retain the most important pixels for the task.
Ranked #4 on
Instance Segmentation
on Cityscapes val
FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction
Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency.
LivePose: Online 3D Reconstruction from Monocular Video with Dynamic Camera Poses
Dense 3D reconstruction from RGB images traditionally assumes static camera pose estimates.
Generative Multiplane Images: Making a 2D GAN 3D-Aware
What is really needed to make an existing 2D GAN 3D-aware?
Fast and Explicit Neural View Synthesis
We study the problem of novel view synthesis from sparse source observations of a scene comprised of 3D objects.
Deep Neural Network Approach for Annual Luminance Simulations
The proposed DNN model can faithfully predict high-quality annual panoramic luminance maps from one of the three options within 30 minutes training time: a) point-in-time luminance imagery spanning 5% of the year, when evenly distributed during daylight hours, b) one-month hourly imagery generated or collected continuously during daylight hours around the equinoxes (8% of the year); or c) 9 days of hourly data collected around the spring equinox, summer and winter solstices (2. 5% of the year) all suffice to predict the luminance maps for the rest of the year.
Equivariant Neural Rendering
We propose a framework for learning neural scene representations directly from images, without 3D supervision.
Learning Stylized Character Expressions from Humans
We present DeepExpr, a novel expression transfer system from humans to multiple stylized characters via deep learning.
Manhattan Room Layout Reconstruction from a Single 360 image: A Comparative Study of State-of-the-art Methods
Recent approaches for predicting layouts from 360 panoramas produce excellent results.
LayoutNet: Reconstructing the 3D Room Layout from a Single RGB Image
We propose an algorithm to predict room layout from a single image that generalizes across panoramas and perspective images, cuboid layouts and more general layouts (e. g. L-shape room).
