On a high level, DiffusionRig learns to map simplistic renderings of 3D face models to realistic photos of a given person.
The loopback between two branches enables the category label to supervise the cell locating branch to learn the locating ability for cancerous areas.
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.
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.
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.
This enables the rendering of novel views of the object under arbitrary environment lighting and editing of the object's material properties.
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
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.
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.
The light stage has been widely used in computer graphics for the past two decades, primarily to enable the relighting of human faces.
1 code implementation • 9 Aug 2020 • Xiuming Zhang, Sean Fanello, Yun-Ta Tsai, Tiancheng Sun, Tianfan Xue, Rohit Pandey, Sergio Orts-Escolano, Philip Davidson, Christoph Rhemann, Paul Debevec, Jonathan T. Barron, Ravi Ramamoorthi, William T. Freeman
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.
We study the inverse graphics problem of inferring a holistic representation for natural images.
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.
Humans are capable of building holistic representations for images at various levels, from local objects, to pairwise relations, to global structures.
From a single image, humans are able to perceive the full 3D shape of an object by exploiting learned shape priors from everyday life.
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.
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.
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