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Found 20 papers, 1 papers with code
Instruct-Imagen: Image Generation with Multi-modal Instruction
We introduce *multi-modal instruction* for image generation, a task representation articulating a range of generation intents with precision.
Fine-grained Controllable Video Generation via Object Appearance and Context
To achieve detailed control, we propose a unified framework to jointly inject control signals into the existing text-to-video model.
Towards Authentic Face Restoration with Iterative Diffusion Models and Beyond
An authentic face restoration system is becoming increasingly demanding in many computer vision applications, e. g., image enhancement, video communication, and taking portrait.
Controllable One-Shot Face Video Synthesis With Semantic Aware Prior
The one-shot talking-head synthesis task aims to animate a source image to another pose and expression, which is dictated by a driving frame.
Video Generation Beyond a Single Clip
We tackle the long video generation problem, i. e.~generating videos beyond the output length of video generation models.
Identity Encoder for Personalized Diffusion
Our approach greatly reduces the overhead for personalized image generation and is more applicable in many potential applications.
Taming Encoder for Zero Fine-tuning Image Customization with Text-to-Image Diffusion Models
This paper proposes a method for generating images of customized objects specified by users.
Rethinking Deep Face Restoration
While existing approaches for face restoration make significant progress in generating high-quality faces, they often fail to preserve facial features and cannot authentically reconstruct the faces.
2.5D Visual Relationship Detection
To enable progress on this task, we create a new dataset consisting of 220k human-annotated 2. 5D relationships among 512K objects from 11K images.
Camera View Adjustment Prediction for Improving Image Composition
To address this issue, we propose a deep learning-based approach that provides suggestions to the photographer on how to adjust the camera view before capturing.
Kernel Transformer Networks for Compact Spherical Convolution
KTNs efficiently transfer convolution kernels from perspective images to the equirectangular projection of 360{\deg} images.
Learning Compressible 360° Video Isomers
Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360° video as well, with reasonable results.
Learning Compressible 360° Video Isomers
Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360{\deg} video as well, with reasonable results.
Learning Spherical Convolution for Fast Features from 360° Imagery
While 360{\deg} cameras offer tremendous new possibilities in vision, graphics, and augmented reality, the spherical images they produce make core feature extraction non-trivial.
Making 360deg Video Watchable in 2D: Learning Videography for Click Free Viewing
360deg video requires human viewers to actively control "where" to look while watching the video.
Making 360$^{\circ}$ Video Watchable in 2D: Learning Videography for Click Free Viewing
360$^{\circ}$ video requires human viewers to actively control "where" to look while watching the video.
Pano2Vid: Automatic Cinematography for Watching 360$^{\circ}$ Videos
AutoCam leverages NFOV web video to discriminatively identify space-time "glimpses" of interest at each time instant, and then uses dynamic programming to select optimal human-like camera trajectories.
Detecting Engagement in Egocentric Video
In a wearable camera video, we see what the camera wearer sees.
Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video
Current approaches for activity recognition often ignore constraints on computational resources: 1) they rely on extensive feature computation to obtain rich descriptors on all frames, and 2) they assume batch-mode access to the entire test video at once.
Transfer Learning for Video Recognition with Scarce Training Data for Deep Convolutional Neural Network
The same lack-of-training-sample problem limits the usage of deep models on a wide range of computer vision problems where obtaining training data are difficult.
