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