Search Results for author:
Found 7 papers, 2 papers with code
Omnimatte3D: Associating Objects and Their Effects in Unconstrained Monocular Video
Instead, our method applies recent progress in monocular camera pose and depth estimation to create a full, RGBD video layer for the background, along with a video layer for each foreground object.
Generalizable Patch-Based Neural Rendering
Neural rendering has received tremendous attention since the advent of Neural Radiance Fields (NeRF), and has pushed the state-of-the-art on novel-view synthesis considerably.
Light Field Neural Rendering
Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene.
Generalized Fourier Features for Coordinate-Based Learning of Functions on Manifolds
In this work, we generalize positional encoding with Fourier features to non-Euclidean manifolds.
Segmentation-grounded Scene Graph Generation
Our framework is agnostic to the underlying scene graph generation method and address the lack of segmentation annotations in target scene graph datasets (e. g., Visual Genome) through transfer and multi-task learning from, and with, an auxiliary dataset (e. g., MS COCO).
Energy-Based Learning for Scene Graph Generation
The proposed formulation allows for efficiently incorporating the structure of scene graphs in the output space.
Ranked #3 on
Scene Graph Classification
on Visual Genome
(R@20 metric)
Mixture-Kernel Graph Attention Network for Situation Recognition
Understanding images beyond salient actions involves reasoning about scene context, objects, and the roles they play in the captured event.
Ranked #4 on
Grounded Situation Recognition
on SWiG
