arXiv:2608. 19723v1 Announce Type: cross Abstract: Streaming video understanding requires models to causally update state as video arrives and organize growing history into semantic units that can evolve, persist, and be recalled under bounded computation and memory.
arXiv:2609.38428v1 Announce Type: new Abstract: Real-time commentary for Multiplayer Online Battle Arena (MOBA) esports requires a vision-language model (VLM) to narrate a live match second by second...
The paper introduces a new benchmark of 345 annotated tennis ball bounces and racket hits across eight video clips featuring diverse viewpoints, including broadcast, fixed elevated, phone, and court-level angles. It evaluates several event detection methods—supervised spotters, zero-shot vision-language models, and a parameter-free arbitration layer called RallyArb—showing that supervised models perform well on broadcast footage but poorly on non-broadcast views, while RallyArb maintains high bounce detection across all viewpoints but struggles with precise hit timing. The dataset, evaluation tools, and frozen predictions are publicly released to support further research.
arXiv:2609.28049v1 Announce Type: cross Abstract: Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as ev...
arXiv:2607. 11548v1 Announce Type: cross Abstract: Spatial football metrics such as pitch control assume access to the positions of all 22 players, yet the most widely available source of positional data -- the broadcast main camera -- shows only 10-16 of them at any moment.
arXiv:2608. 14016v1 Announce Type: cross Abstract: Live game commentary is scarce: it exists for professional esports broadcasts and almost nowhere else.
Video understanding is usually benchmarked on curated, single-actor, or professionally filmed clips, and a strong score there is routinely read as evidence a model is robust enough for deployment. Ama...
WanPE is a 397‑B parameter prompt‑enhancement model that learns director‑level cinematic planning from 1.05 M real‑world videos. It generates shot‑level cinematic plans through video‑grounded reverse construction and uses Semantic‑Consistency GRPO (SC‑GRPO) to maintain user intent across shots and time. In evaluations, WanPE improves human preference over raw prompts by up to 50.86 points for 30‑second videos and outperforms commercial offerings for shorter durations.
arXiv:2604.24317v2 Announce Type: replace Abstract: Streaming video models should respond at the moment an event unfolds, not after the moment has passed. Yet existing online VideoQA benchmarks remai...
arXiv:2603.02655v2 Announce Type: replace-cross Abstract: Real-time video commentary generation provides textual descriptions of ongoing events in videos. It supports accessibility and engagement in...
The paper introduces a zero‑shot multimodal framework for spotting soccer events in full‑length broadcasts without task‑specific training. It segments matches into overlapping clips, represents them with a compact visual storyboard and aligned commentary, and processes these jointly with a general‑purpose multimodal model. On three annotated matches, the best setup achieves 72.0% F1@5s, surpassing SoccerNet‑trained baselines, and the storyboard reduces inference cost by 72%. "whyItMatters":"The approach enables efficient, high‑accuracy event detection and highlight generation in soccer broadcasts without relying on large annotated datasets or soccer‑specific training, as demonstrated by its superior performance and reduced inference cost."
arXiv:2606. 06991v1 Announce Type: cross Abstract: Online Video Large Language Models (Video-LLMs) have advanced toward seamless human-AI interaction through frame-by-frame processing and proactive responding.