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Found 17 papers, 4 papers with code
LEDiff: Latent Exposure Diffusion for HDR Generation
While consumer displays increasingly support more than 10 stops of dynamic range, most image assets such as internet photographs and generative AI content remain limited to 8-bit low dynamic range (LDR), constraining their utility across high dynamic range (HDR) applications.
Instruction-based Image Manipulation by Watching How Things Move
This paper introduces a novel dataset construction pipeline that samples pairs of frames from videos and uses multimodal large language models (MLLMs) to generate editing instructions for training instruction-based image manipulation models.
Generative Portrait Shadow Removal
For robust and natural shadow removal, we propose to train the diffusion model with a compositional repurposing framework: a pre-trained text-guided image generation model is first fine-tuned to harmonize the lighting and color of the foreground with a background scene by using a background harmonization dataset; and then the model is further fine-tuned to generate a shadow-free portrait image via a shadow-paired dataset.
COMPOSE: Comprehensive Portrait Shadow Editing
Existing portrait relighting methods struggle with precise control over facial shadows, particularly when faced with challenges such as handling hard shadows from directional light sources or adjusting shadows while remaining in harmony with existing lighting conditions.
Explorative Inbetweening of Time and Space
We introduce bounded generation as a generalized task to control video generation to synthesize arbitrary camera and subject motion based only on a given start and end frame.
Magic Fixup: Streamlining Photo Editing by Watching Dynamic Videos
We show that by using simple segmentations and coarse 2D manipulations, we can synthesize a photorealistic edit faithful to the user's input while addressing second-order effects like harmonizing the lighting and physical interactions between edited objects.
Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image
At the core of portrait photography is the search for ideal lighting and viewpoint.
Dr. Bokeh: DiffeRentiable Occlusion-aware Bokeh Rendering
Dr. Bokeh then takes the layered representation and user-defined lens parameters to render photo-realistic lens blur based on the novel occlusion-aware bokeh rendering method.
Restoration by Generation with Constrained Priors
We propose a method to adapt a pretrained diffusion model for image restoration by simply adding noise to the input image to be restored and then denoise.
DiffusionRig: Learning Personalized Priors for Facial Appearance Editing
On a high level, DiffusionRig learns to map simplistic renderings of 3D face models to realistic photos of a given person.
Automatic High Resolution Wire Segmentation and Removal
In this paper, we present an automatic wire clean-up system that eases the process of wire segmentation and removal/inpainting to within a few seconds.
LightPainter: Interactive Portrait Relighting with Freehand Scribble
Recent portrait relighting methods have achieved realistic results of portrait lighting effects given a desired lighting representation such as an environment map.
SunStage: Portrait Reconstruction and Relighting using the Sun as a Light Stage
Our method only requires the user to capture a selfie video outdoors, rotating in place, and uses the varying angles between the sun and the face as guidance in joint reconstruction of facial geometry, reflectance, camera pose, and lighting parameters.
Time-Travel Rephotography
Many historical people were only ever captured by old, faded, black and white photos, that are distorted due to the limitations of early cameras and the passage of time.
Zoom to Learn, Learn to Zoom
We show how to obtain such ground-truth data via optical zoom and contribute a dataset, SR-RAW, for real-world computational zoom.
Synthetic Defocus and Look-Ahead Autofocus for Casual Videography
We present a system that synthetically renders refocusable video from a deep DOF video shot with a smartphone, and analyzes future video frames to deliver context-aware autofocus for the current frame.
Single Image Reflection Separation with Perceptual Losses
Our loss function includes two perceptual losses: a feature loss from a visual perception network, and an adversarial loss that encodes characteristics of images in the transmission layers.
