A great example of how domain-specific knowledge can help design network architecture, in this case helping them make the jump from supervised learning (where training data may be difficult or time-consuming to acquire) to unsupervised learning (where training data is often plentiful). Modern 3D human pose estimation techniques rely on deep networks, which require large…Read More
Month: April 2018
Deep Unsupervised Intrinsic Image Decomposition by Siamese Training
Intrinsic image decomposition means splitting the observed color of a scene into its underlying components, such as illumination and reflectance. Once this process has been performed the layers can be manipulated independently before being recomposed to recreate a modified scene. What’s particularly interesting about this work is that it uses unsupervised training, which by definition…Read More
Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation
Although this technique to estimate camera motion and decompose the scene into rigid/dynamic motion (a potential aid to segmentation) relies on a depth channel in the input images, it may not be long until new approaches are developed which can operate on RGB only. Estimation of 3D motion in a dynamic scene from a temporal…Read More
Learning Category-Specific Mesh Reconstruction from Image Collections
Some truly remarkable results given the dataset from which this model was trained. Code on github will be available in the future. We present a learning framework for recovering the 3D shape, camera, and texture of an object from a single image. The shape is represented as a deformable 3D mesh model of an object…Read More