AI Technology: Vision (image recognition, image generation, text recognition OCR, etc.)
The rapid growth of video data demands advanced methods for fine-grained understanding and high-fidelity generation, yet current models struggle with complex scenarios involving continuous actions, multi-shot compositions, and dynamic camera movements. Issues like color jitter, temporal inconsistencies, and occlusion handling highlight the need for improved spatio-temporal representations and efficient handling of high-dimensional video data. This project proposes a video tokenizer based on Variational Autoencoders (VAEs) to address these challenges, incorporating spatio-temporal attention mechanisms and transformer-based architectures to manage long-range spatio-temporal dependencies and reduce artifacts.
Advanced loss functions, including temporal warping loss and motion consistency loss, alongside some regularization, ensure improved perceptual compression and a smooth latent space. The video tokenizer will be validated through integration with latent diffusion frameworks, enhancing generative models for more accurate, high-quality video generation. Its scalable architecture could efficiently handle high-resolution video and complex motions. Comprehensive validation, including ablation studies and benchmark testing, will ensure reliability and generalizability, positioning the tokenizer as a foundational model for high-performance video reconstruction and generation.
Tinne Tuytelaars, KU Leuven, Belgium