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Found 10 papers, 3 papers with code
Replay: Multi-modal Multi-view Acted Videos for Casual Holography
We introduce Replay, a collection of multi-view, multi-modal videos of humans interacting socially.
Novel-View Acoustic Synthesis
We introduce the novel-view acoustic synthesis (NVAS) task: given the sight and sound observed at a source viewpoint, can we synthesize the sound of that scene from an unseen target viewpoint?
Common Pets in 3D: Dynamic New-View Synthesis of Real-Life Deformable Categories
Obtaining photorealistic reconstructions of objects from sparse views is inherently ambiguous and can only be achieved by learning suitable reconstruction priors.
KeyTr: Keypoint Transporter for 3D Reconstruction of Deformable Objects in Videos
Compared to weaker deformation models, this significantly reduces the reconstruction ambiguity and, for dynamic objects, allows Keypoint Transporter to obtain reconstructions of the quality superior or at least comparable to prior approaches while being much faster and reliant on a pre-trained monocular depth estimator network.
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data.
DensePose 3D: Lifting Canonical Surface Maps of Articulated Objects to the Third Dimension
The method learns, in an end-to-end fashion, a soft partition of a given category-specific 3D template mesh into rigid parts together with a monocular reconstruction network that predicts the part motions such that they reproject correctly onto 2D DensePose-like surface annotations of the object.
Unsupervised Learning of 3D Object Categories from Videos in the Wild
Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D.
Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction
We propose the Canonical 3D Deformer Map, a new representation of the 3D shape of common object categories that can be learned from a collection of 2D images of independent objects.
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Structured-output learning is a challenging problem; particularly so because of the difficulty in obtaining large datasets of fully labelled instances for training.
Spatial Inference Machines
Experimental results show that the spatial dependencies learned by our method significantly improve the accuracy of segmentation.
