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Found 16 papers, 11 papers with code
DiffBody: Human Body Restoration by Imagining with Generative Diffusion Prior
Human body restoration plays a vital role in various applications related to the human body.
Within the Dynamic Context: Inertia-aware 3D Human Modeling with Pose Sequence
Neural rendering techniques have significantly advanced 3D human body modeling.
Clearer Frames, Anytime: Resolving Velocity Ambiguity in Video Frame Interpolation
Existing video frame interpolation (VFI) methods blindly predict where each object is at a specific timestep t ("time indexing"), which struggles to predict precise object movements.
Fooling Polarization-based Vision using Locally Controllable Polarizing Projection
If so, is that possible to realize these adversarial attacks in the physical world, without being perceived by human eyes?
Visibility Constrained Wide-band Illumination Spectrum Design for Seeing-in-the-Dark
Seeing-in-the-dark is one of the most important and challenging computer vision tasks due to its wide applications and extreme complexities of in-the-wild scenarios.
Rethinking Video Frame Interpolation from Shutter Mode Induced Degradation
Image restoration from various motion-related degradations, like blurry effects recorded by a global shutter (GS) and jello effects caused by a rolling shutter (RS), has been extensively studied.
NIR-assisted Video Enhancement via Unpaired 24-hour Data
In this paper, we defend the feasibility and superiority of NIR-assisted low-light video enhancement results by using unpaired 24-hour data for the first time, which significantly eases data collection and improves generalization performance on in-the-wild data.
Blur Interpolation Transformer for Real-World Motion from Blur
This paper studies the challenging problem of recovering motion from blur, also known as joint deblurring and interpolation or blur temporal super-resolution.
ClipCrop: Conditioned Cropping Driven by Vision-Language Model
Image cropping has progressed tremendously under the data-driven paradigm.
Towards Real-World Video Deblurring by Exploring Blur Formation Process
We believe the novel realistic synthesis pipeline and the corresponding RAW video dataset can help the community to easily construct customized blur datasets to improve real-world video deblurring performance largely, instead of laboriously collecting real data pairs.
Efficient Video Deblurring Guided by Motion Magnitude
Video deblurring is a highly under-constrained problem due to the spatially and temporally varying blur.
Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance
Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region.
Learning Adaptive Warping for Real-World Rolling Shutter Correction
This paper proposes the first real-world rolling shutter (RS) correction dataset, BS-RSC, and a corresponding model to correct the RS frames in a distorted video.
Bringing Rolling Shutter Images Alive with Dual Reversed Distortion
In this paper, instead of two consecutive frames, we propose to exploit a pair of images captured by dual RS cameras with reversed RS directions for this highly challenging task.
Real-world Video Deblurring: A Benchmark Dataset and An Efficient Recurrent Neural Network
Real-world video deblurring in real time still remains a challenging task due to the complexity of spatially and temporally varying blur itself and the requirement of low computational cost.
Ranked #34 on
Image Deblurring
on GoPro
(using extra training data)
Towards Rolling Shutter Correction and Deblurring in Dynamic Scenes
Since direct application of existing individual rolling shutter correction (RSC) or global shutter deblurring (GSD) methods on RSCD leads to undesirable results due to inherent flaws in the network architecture, we further present the first learning-based model (JCD) for RSCD.
