Search Results for author:
Found 10 papers, 5 papers with code
XScale-NVS: Cross-Scale Novel View Synthesis with Hash Featurized Manifold
We also introduce a novel dataset, namely GigaNVS, to benchmark cross-scale, high-resolution novel view synthesis of realworld large-scale scenes.
Beyond Talking -- Generating Holistic 3D Human Dyadic Motion for Communication
Furthermore, we introduce the HoCo holistic communication dataset, which is a valuable resource for future research.
OmniSeg3D: Omniversal 3D Segmentation via Hierarchical Contrastive Learning
Towards holistic understanding of 3D scenes, a general 3D segmentation method is needed that can segment diverse objects without restrictions on object quantity or categories, while also reflecting the inherent hierarchical structure.
Spatially and Spectrally Consistent Deep Functional Maps
We first justify that under certain conditions, the learned maps, when represented in the spectral domain, are already cycle consistent.
Neural Intrinsic Embedding for Non-rigid Point Cloud Matching
As a primitive 3D data representation, point clouds are prevailing in 3D sensing, yet short of intrinsic structural information of the underlying objects.
RealGraph: A Multiview Dataset for 4D Real-world Context Graph Generation
The CGG task capitalizes on the calibrated multiview videos of a dynamic scene, and targets at recovering semantic information (coordination, trajectories and relationships) of the presented objects in the form of spatio-temporal context graph in 4D space.
Optical Neural Ordinary Differential Equations
Increasing the layer number of on-chip photonic neural networks (PNNs) is essential to improve its model performance.
Cross-Camera Deep Colorization
Our method takes cross-domain and cross-scale images as input, and consequently synthesizes HR colorization results to facilitate the trade-off between spatial-temporal resolution and color depth in the single-camera imaging system.
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation
In this paper, we consider the problem of reference-based video super-resolution(RefVSR), i. e., how to utilize a high-resolution (HR) reference frame to super-resolve a low-resolution (LR) video sequence.
Reference-based Video Super-Resolution
Video Super-Resolution
OperatorNet: Recovering 3D Shapes From Difference Operators
This paper proposes a learning-based framework for reconstructing 3D shapes from functional operators, compactly encoded as small-sized matrices.
