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Found 10 papers, 7 papers with code
SimpleNeRF: Regularizing Sparse Input Neural Radiance Fields with Simpler Solutions
The depth estimated by these simpler models is used to supervise the NeRF depth estimates.
Test Time Adaptation for Blind Image Quality Assessment
In this work, we introduce two novel quality-relevant auxiliary tasks at the batch and sample levels to enable TTA for blind IQA.
ViP-NeRF: Visibility Prior for Sparse Input Neural Radiance Fields
We reformulate the NeRF to also directly output the visibility of a 3D point from a given viewpoint to reduce the training time with the visibility constraint.
Semi-supervised Learning of Perceptual Video Quality by Generating Consistent Pairwise Pseudo-Ranks
Designing learning-based no-reference (NR) video quality assessment (VQA) algorithms for camera-captured videos is cumbersome due to the requirement of a large number of human annotations of quality.
Low Light Video Enhancement by Learning on Static Videos with Cross-Frame Attention
The design of deep learning methods for low light video enhancement remains a challenging problem owing to the difficulty in capturing low light and ground truth video pairs.
Temporal View Synthesis of Dynamic Scenes through 3D Object Motion Estimation with Multi-Plane Images
The predicted object motion is then integrated with the given user or camera motion to generate the next frame.
Ranked #1 on
Video Prediction
on MPI Sintel
Multiview Contrastive Learning for Completely Blind Video Quality Assessment of User Generated Content
Completely blind video quality assessment (VQA) refers to a class of quality assessment methods that do not use any reference videos, human opinion scores or training videos from the target database to learn a quality model.
Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model
Challenges in capturing aligned low light and well-lit image pairs and collecting a large number of human opinion scores of quality for training, warrant the design of unsupervised (or opinion unaware) no-reference (NR) QA methods.
Revealing Disocclusions in Temporal View Synthesis through Infilling Vector Prediction
We consider the problem of temporal view synthesis, where the goal is to predict a future video frame from the past frames using knowledge of the depth and relative camera motion.
Ranked #1 on
Temporal View Synthesis
on SceneNet RGB-D
Understanding the Perceived Quality of Video Predictions
We show that our feature design leads to state of the art quality prediction in accordance with human judgments on our IISc PVQA Database.
