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Found 8 papers, 2 papers with code
FaceFolds: Meshed Radiance Manifolds for Efficient Volumetric Rendering of Dynamic Faces
3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts$\unicode{x2014}$photorealism, efficiency, compatibility, and configurability.
Sandwiched Compression: Repurposing Standard Codecs with Neural Network Wrappers
Through a variety of examples, we apply the sandwich architecture to sources with different numbers of channels, higher resolution, higher dynamic range, and perceptual distortion measures.
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.
Deep Implicit Volume Compression
We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures.
Volumetric Capture of Humans with a Single RGBD Camera via Semi-Parametric Learning
The key insight is to leverage previously seen "calibration" images of a given user to extrapolate what should be rendered in a novel viewpoint from the data available in the sensor.
LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering
We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.
Low Compute and Fully Parallel Computer Vision With HashMatch
Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.
UltraStereo: Efficient Learning-Based Matching for Active Stereo Systems
Efficient estimation of depth from pairs of stereo images is one of the core problems in computer vision.
