Video Understanding

Boosting video temporal grounding, NumPro empowers Vid-LLMs by adding frame numbers, making temporal localization as easy as flipping through manga.

Sharingan extracts user action sequences from desktop recordings using novel VLM-based methods, achieving 70-80% accuracy and enabling RPA.

EgoVid-5M: First high-quality dataset for egocentric video generation, enabling realistic human-centric world simulations.

ReCapture generates videos with novel camera angles from user videos using masked video fine-tuning, preserving scene motion and plausibly hallucinating unseen parts.

Scaling video generation models doesn’t guarantee they’ll learn physics; this study reveals they prioritize visual cues over true physical understanding.

Adaptive Caching (AdaCache) dramatically speeds up video generation with diffusion transformers by cleverly caching and reusing computations, tailoring the process to each video’s complexity and motio…

High-performing video representation models can be trained using only synthetic videos and images, eliminating the need for large natural video datasets.