Search Results for author:
Found 24 papers, 11 papers with code
Learning Continuous 3D Words for Text-to-Image Generation
We do this by engineering special sets of input tokens that can be transformed in a continuous manner -- we call them Continuous 3D Words.
3DMiner: Discovering Shapes from Large-Scale Unannotated Image Datasets
We present 3DMiner -- a pipeline for mining 3D shapes from challenging large-scale unannotated image datasets.
GAIT: Generating Aesthetic Indoor Tours with Deep Reinforcement Learning
Placing and orienting a camera to compose aesthetically meaningful shots of a scene is not only a key objective in real-world photography and cinematography but also for virtual content creation.
Recovering Detail in 3D Shapes Using Disparity Maps
We present a fine-tuning method to improve the appearance of 3D geometries reconstructed from single images.
ANISE: Assembly-based Neural Implicit Surface rEconstruction
Reconstructions can be obtained in two ways: (i) by directly decoding the part latent codes to part implicit functions, then combining them into the final shape; or (ii) by using part latents to retrieve similar part instances in a part database and assembling them in a single shape.
CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Clouds
We present Cascaded Primitive Fitting Networks (CPFN) that relies on an adaptive patch sampling network to assemble detection results of global and local primitive detection networks.
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of meta-handles for each shape, which are represented as combinations of the given handles.
FAME: 3D Shape Generation via Functionality-Aware Model Evolution
Our tool supports constrained modeling, allowing users to restrict or steer the model evolution with functionality labels.
Graphics
Learning Generative Models of Shape Handles
We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations.
Training a Constrained Natural Media Painting Agent using Reinforcement Learning
We present a novel approach to train a natural media painting using reinforcement learning.
LPaintB: Learning to Paint from Self-Supervision
We present a novel reinforcement learning-based natural media painting algorithm.
DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction
Reconstructing 3D shapes from single-view images has been a long-standing research problem.
Ranked #1 on
Single-View 3D Reconstruction
on ShapeNetCore
3DN: 3D Deformation Network
Given such a source 3D model and a target which can be a 2D image, 3D model, or a point cloud acquired as a depth scan, we introduce 3DN, an end-to-end network that deforms the source model to resemble the target.
Photo-Sketching: Inferring Contour Drawings from Images
Edges, boundaries and contours are important subjects of study in both computer graphics and computer vision.
Sequence-to-Segment Networks for Segment Detection
Detecting segments of interest from an input sequence is a challenging problem which often requires not only good knowledge of individual target segments, but also contextual understanding of the entire input sequence and the relationships between the target segments.
Personalized Image Aesthetics
To accommodate our study, we first collect two distinct datasets, a large image dataset from Flickr and annotated by Amazon Mechanical Turk, and a small dataset of real personal albums rated by owners.
Recognizing and Curating Photo Albums via Event-Specific Image Importance
Automatic organization of personal photos is a problem with many real world ap- plications, and can be divided into two main tasks: recognizing the event type of the photo collection, and selecting interesting images from the collection.
Learning to Detect Multiple Photographic Defects
Our new dataset enables us to formulate the problem as a multi-task learning problem and train a multi-column deep convolutional neural network (CNN) to simultaneously predict the severity of all the defects.
Salient Object Subitizing
We study the problem of Salient Object Subitizing, i. e. predicting the existence and the number of salient objects in an image using holistic cues.
Photo Aesthetics Ranking Network with Attributes and Content Adaptation
In this work, we propose to learn a deep convolutional neural network to rank photo aesthetics in which the relative ranking of photo aesthetics are directly modeled in the loss function.
Ranked #7 on
Aesthetics Quality Assessment
on AVA
Unconstrained Salient Object Detection via Proposal Subset Optimization
Our system leverages a Convolutional-Neural-Network model to generate location proposals of salient objects.
Event-Specific Image Importance
In this paper, we show that the selection of important images is consistent among different viewers, and that this selection process is related to the event type of the album.
Minimum Barrier Salient Object Detection at 80 FPS
Powered by this fast MBD transform algorithm, the proposed salient object detection method runs at 80 FPS, and significantly outperforms previous methods with similar speed on four large benchmark datasets, and achieves comparable or better performance than state-of-the-art methods.
Ranked #6 on
Video Salient Object Detection
on VOS-T
(using extra training data)
Deep Multi-Patch Aggregation Network for Image Style, Aesthetics, and Quality Estimation
We propose a deep multi-patch aggregation network training approach, which allows us to train models using multiple patches generated from one image.
Ranked #8 on
Aesthetics Quality Assessment
on AVA
