Experiments demonstrate that NeuRay can quickly generate high-quality novel view images of unseen scenes with little finetuning and can handle complex scenes with severe self-occlusions which previous methods struggle with.
Moreover, the learned blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model.
To this end, we propose Neural Body, a new human body representation which assumes that the learned neural representations at different frames share the same set of latent codes anchored to a deformable mesh, so that the observations across frames can be naturally integrated.
This paper proposes a doubly robust two-stage semiparametric difference-in-difference estimator for estimating heterogeneous treatment effects with high-dimensional data.
Based on deep snake, we develop a two-stage pipeline for instance segmentation: initial contour proposal and contour deformation, which can handle errors in object localization.
Ranked #1 on Semantic Contour Prediction on Sbd val
Instead of feature pooling, we use group convolutions to exploit underlying structures of the extracted features on the group, resulting in descriptors that are both discriminative and provably invariant to the group of transformations.
We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation.
Ranked #1 on 6D Pose Estimation using RGB on YCB-Video (Mean AUC metric)