Search Results for author:
Found 19 papers, 13 papers with code
Consistent Human Image and Video Generation with Spatially Conditioned Diffusion
By fine-tuning existing base diffusion models on human video data, our method demonstrates strong generalization to unseen human identities and poses without requiring additional per-instance fine-tuning.
OmniDrag: Enabling Motion Control for Omnidirectional Image-to-Video Generation
As virtual reality gains popularity, the demand for controllable creation of immersive and dynamic omnidirectional videos (ODVs) is increasing.
ReVideo: Remake a Video with Motion and Content Control
In this paper, we present a novel attempt to Remake a Video (ReVideo) which stands out from existing methods by allowing precise video editing in specific areas through the specification of both content and motion.
Diffusion-Based Hierarchical Image Steganography
This paper introduces Hierarchical Image Steganography, a novel method that enhances the security and capacity of embedding multiple images into a single container using diffusion models.
DiffEditor: Boosting Accuracy and Flexibility on Diffusion-based Image Editing
Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years.
360DVD: Controllable Panorama Video Generation with 360-Degree Video Diffusion Model
In this paper, we propose a pipeline named 360-Degree Video Diffusion model (360DVD) for generating 360-degree panoramic videos based on the given prompts and motion conditions.
Neural Video Fields Editing
Specifically, we construct a neural video field, powered by tri-plane and sparse grid, to enable encoding long videos with hundreds of frames in a memory-efficient manner.
DragonDiffusion: Enabling Drag-style Manipulation on Diffusion Models
Specifically, we construct classifier guidance based on the strong correspondence of intermediate features in the diffusion model.
Optimization-Inspired Cross-Attention Transformer for Compressive Sensing
And, PGCA block achieves an enhanced information interaction, which introduces the inertia force into the gradient descent step through a cross attention block.
Large-capacity and Flexible Video Steganography via Invertible Neural Network
For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN).
T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models
In this paper, we aim to ``dig out" the capabilities that T2I models have implicitly learned, and then explicitly use them to control the generation more granularly.
TransCL: Transformer Makes Strong and Flexible Compressive Learning
Compressive learning (CL) is an emerging framework that integrates signal acquisition via compressed sensing (CS) and machine learning for inference tasks directly on a small number of measurements.
MM-RealSR: Metric Learning based Interactive Modulation for Real-World Super-Resolution
Instead of using known degradation levels as explicit supervision to the interactive mechanism, we propose a metric learning strategy to map the unquantifiable degradation levels in real-world scenarios to a metric space, which is trained in an unsupervised manner.
Deep Generalized Unfolding Networks for Image Restoration
Concretely, without loss of interpretability, we integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm, driving it to deal with complex and real-world image degradation.
Ranked #5 on
Image Restoration
on CDD-11
Robust Invertible Image Steganography
Previous image steganography methods are limited in hiding capacity and robustness, commonly vulnerable to distortion on container images such as Gaussian noise, Poisson noise, and lossy compression.
Dense Deep Unfolding Network with 3D-CNN Prior for Snapshot Compressive Imaging
To better exploit the spatial-temporal correlation among frames and address the problem of information loss between adjacent phases in existing DUNs, we propose to adopt the 3D-CNN prior in our proximal mapping module and develop a novel dense feature map (DFM) strategy, respectively.
Dynamic Attentive Graph Learning for Image Restoration
Specifically, we propose an improved graph model to perform patch-wise graph convolution with a dynamic and adaptive number of neighbors for each node.
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.
COLA-Net: Collaborative Attention Network for Image Restoration
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance.
