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Found 20 papers, 7 papers with code
SEW: Self-calibration Enhanced Whole Slide Pathology Image Analysis
Pathology images are considered the "gold standard" for cancer diagnosis and treatment, with gigapixel images providing extensive tissue and cellular information.
Efficient and Comprehensive Feature Extraction in Large Vision-Language Model for Clinical Pathology Analysis
Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis, relying heavily on detailed, multi-scale analysis of high-resolution whole slide images (WSI).
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.
A Loopback Network for Explainable Microvascular Invasion Classification
The loopback between two branches enables the category label to supervise the cell locating branch to learn the locating ability for cancerous areas.
Bone marrow sparing for cervical cancer radiotherapy on multimodality medical images
Experiments on patient dataset reveal that our proposed method can enhance the multimodal image registration accuracy and efficiency for medical practitioners in sparing BM of cervical cancer radiotherapy.
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.
Edge-competing Pathological Liver Vessel Segmentation with Limited Labels
The diagnosis of MVI needs discovering the vessels that contain hepatocellular carcinoma cells and counting their number in each vessel, which depends heavily on experiences of the doctor, is largely subjective and time-consuming.
NeRFactor: Neural Factorization of Shape and Reflectance Under an Unknown Illumination
This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties.
Ranked #4 on
Image Relighting
on Stanford-ORB
Editing Conditional Radiance Fields
In this paper, we explore enabling user editing of a category-level NeRF - also known as a conditional radiance field - trained on a shape category.
Ranked #1 on
Novel View Synthesis
on PhotoShape
NeRV: Neural Reflectance and Visibility Fields for Relighting and View Synthesis
We present a method that takes as input a set of images of a scene illuminated by unconstrained known lighting, and produces as output a 3D representation that can be rendered from novel viewpoints under arbitrary lighting conditions.
Multi-Plane Program Induction with 3D Box Priors
We consider two important aspects in understanding and editing images: modeling regular, program-like texture or patterns in 2D planes, and 3D posing of these planes in the scene.
Light Stage Super-Resolution: Continuous High-Frequency Relighting
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces.
Neural Light Transport for Relighting and View Synthesis
In particular, we show how to fuse previously seen observations of illuminants and views to synthesize a new image of the same scene under a desired lighting condition from a chosen viewpoint.
Perspective Plane Program Induction from a Single Image
We study the inverse graphics problem of inferring a holistic representation for natural images.
Portrait Shadow Manipulation
We propose a way to explicitly encode facial symmetry and show that our dataset and training procedure enable the model to generalize to images taken in the wild.
Program-Guided Image Manipulators
Humans are capable of building holistic representations for images at various levels, from local objects, to pairwise relations, to global structures.
Learning to Reconstruct Shapes from Unseen Classes
From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life.
MoSculp: Interactive Visualization of Shape and Time
We present a system that allows users to visualize complex human motion via 3D motion sculptures---a representation that conveys the 3D structure swept by a human body as it moves through space.
Learning Shape Priors for Single-View 3D Completion and Reconstruction
The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects.
Pix3D: Dataset and Methods for Single-Image 3D Shape Modeling
We study 3D shape modeling from a single image and make contributions to it in three aspects.
Ranked #1 on
3D Shape Classification
on Pix3D
