The paper examines four leading motion‑based AI‑generated video detectors and finds that three of them suffer from preprocessing and sampling biases that inflate their reported performance. These detectors rely heavily on motion patterns—specifically, the lower inter‑frame movement typical of synthetic videos—so their accuracy drops to near random when tested on datasets lacking this bias or after simple spatial augmentations. In contrast, a frequency‑based detector remains robust across all datasets, indicating that frequency‑domain methods may generalize better for detecting AI‑generated videos.
By Joren Michels, Lode Jorissen, Nick Michiels
Auteur is a language‑driven method that generates human‑centric camera framing for generative video models. It treats shots as framings relative to an actor, encoding shot size, angle, and composition as functions of human pose and motion, and uses a domain‑specific language that converts to standard 6‑DoF camera parameters. A fine‑tuned multimodal large language model acts as a virtual director, mapping natural language descriptions and coarse human motion to sparse DSL keyframes that are interpolated into continuous camera trajectories for video generation.
By Muhammed Burak Kizil, Enes Sanli, Niloy J. Mitra, Xuelin Chen, Erkut Erdem, Aykut Erdem, Duygu Ceylan
arXiv:2606. 16742v1 Announce Type: cross Abstract: With the rapid advancement of video generation models, distinguishing between AI-generated and authentic videos has emerged as a challenging endeavor.
By Renxi Cheng, Jie Gui, Hongsong Wang
The paper introduces a framework for generating multi‑view images of a person within a natural scene, addressing the scarcity of paired multi‑view datasets for human subjects. It evaluates existing diffusion‑based image‑editing models and finds they often hallucinate head‑turn angles, leading to inconsistent backgrounds. To overcome this, the authors propose the Head Scene Rotation Difference (HSRD) metric, which separates camera movement from head pose changes and enables reliable assessment of 3D spatial parallax for constructing high‑quality synthetic datasets.
By Mahir Majid, Young Kyung Kim, Guillermo Sapiro
arXiv:2607.18227v2 Announce Type: replace
Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
By Dingyun Zhang, Lixue Gong, Wei Liu
AVAtt is a free‑viewing eye‑tracking dataset comprising
Images{} paintings from sixteen art movements and five centuries, with
Scanpaths{} scanpaths and
Fixations{} fixations collected from
Participants{} participants. The dataset provides individual scanpaths, fixation timing, eye‑screen distance, fixation and saliency maps, temporal saliency videos, and rich art metadata. A benchmark of classic and deep saliency models shows significant room for improvement, and 44 scanpath‑similarity measures—including a new language‑free image‑encoder family—are ranked.
By Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani, Alessandro Bruno
arXiv:2606. 00101v1 Announce Type: cross Abstract: With the rapid advancement of artificial intelligence generated content (AIGC) technologies, video forgery has become increasingly prevalent, posing new challenges to public discourse and societal security.
By Huidong Feng, Wentao Chen, Jie Chen, Xinqi Cai, Ruolong Ma, Yinglin Zheng, Yuxin Lin, Ming Zeng
The paper investigates how in‑context learning (ICL) in large vision‑language models (LVLMs) can amplify gender bias. Using the VL‑BICLE framework, the authors show that gendered ICL demonstrations shift model bias toward the demonstrated gender, especially in tasks involving gendered language such as image captioning and pronoun prediction. They find that similarity‑based retrieval does not mitigate this bias and that replacing real images with synthetic ones from stable diffusion reduces bias without hurting caption quality.
By Tong Xiang, Noa Garcia, Yuta Nakashima
arXiv:2610.07939v2 Announce Type: replace
Abstract: Due to rapid advances in Generative AI, commercial video generation tools can be used to produce fabricated surveillance footage that can fool both...
By Baptiste Chopin, Thomas Swearingen, Arun Ross, Antitza Dantcheva, Christian Rathgeb
arXiv:2606. 30528v1 Announce Type: cross Abstract: Current generative models, including GANs and diffusion models, have reached an outstanding level of photorealism, posing significant risks to privacy and security.
By Orazio Pontorno, Mattia Litrico, Luca Guarnera, Mario Valerio Giuffrida, Sebastiano Battiato
arXiv:2606. 14297v1 Announce Type: cross Abstract: Developing accurate crowd-counting models for Hajj pilgrimage scenes remains challenging because domain-specific annotated images are scarce and data collection during large gatherings raises privacy concerns.
By Amirah F. Alshammari, Bander A. Alzahrani, Nahed A. Alowidi
arXiv:2610.33377v1 Announce Type: cross
Abstract: Advertisers routinely create multiple duration variants of ads to meet marketing budgets and viewer preferences, a labor-intensive and costly process...
By Wen Xie, Om Rastogi, Sai Siddhartha Vivek Dhir Rangoju, Gijs Overgoor, Yakov Bart, Sarah Ostadabbas