Search Results for author:
Found 11 papers, 3 papers with code
FlowMap: High-Quality Camera Poses, Intrinsics, and Depth via Gradient Descent
This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence.
SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control
To overcome these limitations, we introduce SmartMask, which allows any novice user to create detailed masks for precise object insertion.
Learning to Render Novel Views from Wide-Baseline Stereo Pairs
We conduct extensive comparisons on held-out test scenes across two real-world datasets, significantly outperforming prior work on novel view synthesis from sparse image observations and achieving multi-view-consistent novel view synthesis.
In-N-Out: Faithful 3D GAN Inversion with Volumetric Decomposition for Face Editing
3D-aware GANs offer new capabilities for view synthesis while preserving the editing functionalities of their 2D counterparts.
Algebraic structure of hierarchic first-order reaction networks applicable to models of clone size distribution and stochastic gene expression
Crucially, we identify the fact that the Lie group associated to hierarchic reaction networks decomposes as a wreath product of the groups associated to the subnetworks of the independent and dependent types.
Cross-modal Latent Space Alignment for Image to Avatar Translation
We present a novel method for automatic vectorized avatar generation from a single portrait image.
Paint2Pix: Interactive Painting based Progressive Image Synthesis and Editing
In particular, we propose a novel approach paint2pix, which learns to predict (and adapt) "what a user wants to draw" from rudimentary brushstroke inputs, by learning a mapping from the manifold of incomplete human paintings to their realistic renderings.
Unsupervised Discovery and Composition of Object Light Fields
Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding.
Intelli-Paint: Towards Developing Human-like Painting Agents
However, current research in this direction is often reliant on a progressive grid-based division strategy wherein the agent divides the overall image into successively finer grids, and then proceeds to paint each of them in parallel.
Experimentally testable whole brain manifolds that recapitulate behavior
Thus, as a final validation of how well GMN captures essential dynamic information, we show that the artificially generated time series can be used as a training set to predict out-of-sample observed fly locomotion, as well as brain activity in out of sample withheld data not used in model building.
MaterialGAN: Reflectance Capture using a Generative SVBRDF Model
We address the problem of reconstructing spatially-varying BRDFs from a small set of image measurements.
