Search Results for author:
Found 11 papers, 2 papers with code
GST: Precise 3D Human Body from a Single Image with Gaussian Splatting Transformers
Our key observation is that the vertices of standardized human meshes (such as SMPL) can provide an adequate density and approximate initial position for Gaussians.
Stale Diffusion: Hyper-realistic 5D Movie Generation Using Old-school Methods
As the horses have long left the barn, our proposal may be seen as antiquated and irrelevant.
A Light Touch Approach to Teaching Transformers Multi-view Geometry
Transformers are powerful visual learners, in large part due to their conspicuous lack of manually-specified priors.
Moving SLAM: Fully Unsupervised Deep Learning in Non-Rigid Scenes
We build on the idea of view synthesis, which uses classical camera geometry to re-render a source image from a different point-of-view, specified by a predicted relative pose and depth map.
On the Origin of Species of Self-Supervised Learning
In the quiet backwaters of cs. CV, cs. LG and stat. ML, a cornucopia of new learning systems is emerging from a primordial soup of mathematics-learning systems with no need for external supervision.
Multi-modal Self-Supervision from Generalized Data Transformations
In this paper, we show that, for videos, the answer is more complex, and that better results can be obtained by accounting for the interplay between invariance, distinctiveness, multiple modalities and time.
State-of-Art-Reviewing: A Radical Proposal to Improve Scientific Publication
Peer review forms the backbone of modern scientific manuscript evaluation.
Small Steps and Giant Leaps: Minimal Newton Solvers for Deep Learning
We first validate our method, called CurveBall, on small problems with known solutions (noisy Rosenbrock function and degenerate 2-layer linear networks), where current deep learning solvers struggle.
Substitute Teacher Networks: Learning with Almost No Supervision
Learning through experience is time-consuming, inefficient and often bad for your cortisol levels.
Beyond the Shortest Path : Unsupervised Domain Adaptation by Sampling Subspaces Along the Spline Flow
In this case, the source/target domains are represented in the form of subspaces, which are treated as points on the Grassmann manifold.
Rolling Riemannian Manifolds to Solve the Multi-class Classification Problem
In the past few years there has been a growing interest on geometric frameworks to learn supervised classification models on Riemannian manifolds [31, 27].
