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Found 7 papers, 1 papers with code
Uplift and Upsample: Efficient 3D Human Pose Estimation with Uplifting Transformers
The state-of-the-art for monocular 3D human pose estimation in videos is dominated by the paradigm of 2D-to-3D pose uplifting.
Synchronized Audio-Visual Frames with Fractional Positional Encoding for Transformers in Video-to-Text Translation
Video-to-Text (VTT) is the task of automatically generating descriptions for short audio-visual video clips, which can support visually impaired people to understand scenes of a YouTube video for instance.
Extended Self-Critical Pipeline for Transforming Videos to Text (TRECVID-VTT Task 2021) -- Team: MMCUniAugsburg
For both models, we train on the complete VATEX dataset and 90% of the TRECVID-VTT dataset for pretraining while using the remaining 10% for validation.
Error Bounds of Projection Models in Weakly Supervised 3D Human Pose Estimation
Our paper thus establishes a theoretical baseline that shows the importance of suitable projection models in weakly supervised 3D human pose estimation.
Decoupling Video and Human Motion: Towards Practical Event Detection in Athlete Recordings
In this paper we address the problem of motion event detection in athlete recordings from individual sports.
Mining Automatically Estimated Poses from Video Recordings of Top Athletes
Human pose detection systems based on state-of-the-art DNNs are on the go to be extended, adapted and re-trained to fit the application domain of specific sports.
Activity-conditioned continuous human pose estimation for performance analysis of athletes using the example of swimming
Our main contributions are threefold: (a) We apply and evaluate a fine-tuned Convolutional Pose Machine architecture as a baseline in our very challenging aquatic environment and discuss its error modes, (b) we propose an extension to input swimming style information into the fully convolutional architecture and (c) modify the architecture for continuous pose estimation in videos.
