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Found 34 papers, 12 papers with code
Explore In-Context Segmentation via Latent Diffusion Models
In-context segmentation has drawn more attention with the introduction of vision foundation models.
AToM: Amortized Text-to-Mesh using 2D Diffusion
We introduce Amortized Text-to-Mesh (AToM), a feed-forward text-to-mesh framework optimized across multiple text prompts simultaneously.
DualVAE: Dual Disentangled Variational AutoEncoder for Recommendation
To address this problem, we propose a Dual Disentangled Variational AutoEncoder (DualVAE) for collaborative recommendation, which combines disentangled representation learning with variational inference to facilitate the generation of implicit interaction data.
Diffusion Priors for Dynamic View Synthesis from Monocular Videos
Dynamic novel view synthesis aims to capture the temporal evolution of visual content within videos.
LGMRec: Local and Global Graph Learning for Multimodal Recommendation
The multimodal recommendation has gradually become the infrastructure of online media platforms, enabling them to provide personalized service to users through a joint modeling of user historical behaviors (e. g., purchases, clicks) and item various modalities (e. g., visual and textual).
Virtual Pets: Animatable Animal Generation in 3D Scenes
Toward unlocking the potential of generative models in immersive 4D experiences, we introduce Virtual Pet, a novel pipeline to model realistic and diverse motions for target animal species within a 3D environment.
SceneWiz3D: Towards Text-guided 3D Scene Composition
We are witnessing significant breakthroughs in the technology for generating 3D objects from text.
Controllable Chest X-Ray Report Generation from Longitudinal Representations
Previous approaches to automated radiology reporting generally do not provide the prior study as input, precluding comparison which is required for clinical accuracy in some types of scans, and offer only unreliable methods of interpretability.
Finding-Aware Anatomical Tokens for Chest X-Ray Automated Reporting
Automated approaches to radiology reporting require the image to be encoded into a suitable token representation for input to the language model.
AutoDecoding Latent 3D Diffusion Models
We present a novel approach to the generation of static and articulated 3D assets that has a 3D autodecoder at its core.
Reconstructing Animatable Categories from Videos
Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging, which are difficult to scale to arbitrary categories.
3D Shape Reconstruction from Videos
Dynamic Reconstruction
+1
Flow supervision for Deformable NeRF
In this paper we present a new method for deformable NeRF that can directly use optical flow as supervision.
The role of noise in denoising models for anomaly detection in medical images
Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance.
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level Dependencies
Specifically, for neighborhood-level dependencies, we explicitly consider both popularity score and preference correlation by designing a joint neighborhood-level dependency weight, based on which we construct a neighborhood-level dependencies graph to capture higher-order interaction features.
MBW: Multi-view Bootstrapping in the Wild
Our Multi-view Bootstrapping in the Wild (MBW) approach demonstrates impressive results on standard human datasets, as well as tigers, cheetahs, fish, colobus monkeys, chimpanzees, and flamingos from videos captured casually in a zoo.
3D Reconstruction
Semi-supervised 2D and 3D landmark labeling
+1
Neural Prior for Trajectory Estimation
Here, we propose a neural trajectory prior to capture continuous spatio-temporal information without the need for offline data.
High Fidelity 3D Reconstructions with Limited Physical Views
Multi-view triangulation is the gold standard for 3D reconstruction from 2D correspondences given known calibration and sufficient views.
Neural Trajectory Fields for Dynamic Novel View Synthesis
Recent approaches to render photorealistic views from a limited set of photographs have pushed the boundaries of our interactions with pictures of static scenes.
PAUL: Procrustean Autoencoder for Unsupervised Lifting
Recent success in casting Non-rigid Structure from Motion (NRSfM) as an unsupervised deep learning problem has raised fundamental questions about what novelty in NRSfM prior could the deep learning offer.
SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images
Dense 3D object reconstruction from a single image has recently witnessed remarkable advances, but supervising neural networks with ground-truth 3D shapes is impractical due to the laborious process of creating paired image-shape datasets.
3D Object Reconstruction From A Single Image
3D Reconstruction
A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information
Afterward, by performing a simplified RGCN-based node information propagation on the constructed heterogeneous graph, the embeddings of users and items can be adjusted with textual knowledge, which effectively alleviates the negative effects of data sparsity.
A Text-based Deep Reinforcement Learning Framework for Interactive Recommendation
IRSs usually face the large discrete action space problem, which makes most of the existing RL-based recommendation methods inefficient.
Deep NRSfM++: Towards Unsupervised 2D-3D Lifting in the Wild
The recovery of 3D shape and pose from 2D landmarks stemming from a large ensemble of images can be viewed as a non-rigid structure from motion (NRSfM) problem.
Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning
This alleviates the data bottleneck, which is one of the major concern for supervised methods.
Ranked #21 on
Weakly-supervised 3D Human Pose Estimation
on Human3.6M
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes
We present a fully data-driven method to compute depth from diverse monocular video sequences that contain large amounts of non-rigid objects, e. g., people.
Deep Convolutional Compressed Sensing for LiDAR Depth Completion
In this paper we consider the problem of estimating a dense depth map from a set of sparse LiDAR points.
Learning Depth from Monocular Videos using Direct Methods
The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community.
Semantic Photometric Bundle Adjustment on Natural Sequences
More recently, excellent results have been attained through the application of photometric bundle adjustment (PBA) methods -- which directly minimize the photometric error across frames.
Object-Centric Photometric Bundle Adjustment with Deep Shape Prior
Reconstructing 3D shapes from a sequence of images has long been a problem of interest in computer vision.
Rethinking Reprojection: Closing the Loop for Pose-Aware Shape Reconstruction From a Single Image
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image.
Rethinking Reprojection: Closing the Loop for Pose-aware ShapeReconstruction from a Single Image
An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image.
Deep-LK for Efficient Adaptive Object Tracking
In this paper we present a new approach for efficient regression based object tracking which we refer to as Deep- LK.
Object Proposal by Multi-Branch Hierarchical Segmentation
Hierarchical segmentation based object proposal methods have become an important step in modern object detection paradigm.
An non-uniform sampling strategy for physiological signals component analysis
The conventional approach cannot meet the requirement of physiological signal analysis to extract the main component of the acquired signal.
