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Found 6 papers, 1 papers with code
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution
Video super-resolution (VSR) techniques, especially deep-learning-based algorithms, have drastically improved over the last few years and shown impressive performance on synthetic data.
Edge-aware Consistent Stereo Video Depth Estimation
As a benefit of using stereo inputs, a left-right consistency loss is introduced to improve the performance.
High-Resolution Synthetic RGB-D Datasets for Monocular Depth Estimation
For experiments and analysis, we train the DPT algorithm, a state-of-the-art transformer-based MDE algorithm on the proposed synthetic dataset, which significantly increases the accuracy of depth maps on different scenes by 9 %.
Synthesis of Compositional Animations from Textual Descriptions
Our model can generate plausible pose sequences for short sentences describing single actions as well as long compositional sentences describing multiple sequential and superimposed actions.
Fine-Grained Semantic Segmentation of Motion Capture Data using Dilated Temporal Fully-Convolutional Networks
Human motion capture data has been widely used in data-driven character animation.
Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data
Semantic segmentation of motion capture sequences plays a key part in many data-driven motion synthesis frameworks.
