← Back to all news
Hugging Face Trending Papers

CamWorldQA: Perceptual Quality Assessment of Camera-Controlled World Video Generation

CamWorldQA introduces the first benchmark for assessing the perceptual quality of camera‑controlled world video generation, featuring 720 videos generated by six methods from 20 source videos across six camera trajectories, each scored by human raters. The paper also presents CWQA, a no‑reference quality assessment network that combines spatial, temporal motion, and optical flow features to predict quality scores. Experiments show CWQA outperforms existing VQA methods on the CamWorldQA dataset.

Related stories

arXiv Computer Vision
Sep 16

High-Fidelity Video Quality Assessment with VQA-Specific Saliency

High-Fidelity Video Quality Assessment (HFVQA) is a new framework that uses fixed-size spatio‑temporal patches across multiple scales, including the original resolution, to preserve low‑level quality cues and semantic context. It incorporates a lightweight auxiliary network that learns VQA‑specific saliency directly from quality supervision, enabling the model to focus on the most important spatio‑temporal regions. By combining high‑fidelity cues with task‑specific saliency, HFVQA achieves state‑of‑the‑art performance on standard no‑reference VQA benchmarks while processing only about 12% of the candidate patches, making it computationally efficient.

By Hakan Emre Gedik, Shashank Gupta, Alan Bovik
benchmarks
More like this →
arXiv AI
Sep 16

VOR-Bench: A Human Perception-Driven Benchmark for Video Object Removal

VOR-Bench is a new benchmark for video object removal that addresses shortcomings in current evaluation methods by providing a dataset with paired edited videos and graffiti masks, a realistic motion-capable paired-video acquisition framework (rMPAF), and a perception-driven scoring model (VOR-MDSM). The dataset includes diverse data from model-generated, tool-rendered, and camera-captured sources, ensuring robust real-world assessment. Experiments show that VOR-Bench’s evaluation results correlate strongly (ρ > 0.9) with human subjective judgments, bridging the gap between traditional metrics and human preference.

By Haonan Huang, Tianrui Qiu, Xianghao Zang, Yinan Du, Zhixiang He, Chi Zhang, Hao Sun, Zhongjiang He, Tianwei Cao, Xuchong Zhang, Hongbin Sun, Kongming Liang, Zhanyu Ma
diffusionfine-tuningmultimodalbenchmarks
More like this →
Hugging Face Trending Papers
Aug 13

SNM-VFI: Symmetric Nonlinear Motion-Guided Generative Video Frame Interpolation

We propose Symmetric Nonlinear Motion-guided Generative Video Frame Interpolation (SNM-VFI), a training-free framework for motion-controllable generative video frame interpolation with pre-trained optical flow and video diffusion models. Unlike conventional diffusion-based VFI methods that synthesize intermediate frames from random noise, SNM-VFI guides the generative process with correspondence-aware frames produced by a symmetric nonlinear motion model.

diffusioncomputer-visionbenchmarks
More like this →
arXiv AI
Sep 28

GVCC: Zero-Shot Video Compression via Codebook-Driven Stochastic Rectified Flow

The paper introduces GVCC, a zero‑shot video compression framework that uses a pretrained generative video model as the decoder. GVCC transforms deterministic rectified‑flow samplers into stochastic processes, enabling the transmission of compressed information through per‑step stochastic innovations. The authors evaluate three GVCC variants—Text‑to‑Video, Image‑to‑Video, and First‑Last‑Frame‑to‑Video—on the UVG dataset, reporting perceptual, fidelity, and temporal metrics without claiming global rate‑distortion gains.

By Ziyue Zeng, Xun Su, Haoyuan Liu, Bingyu Lu, Yui Tatsumi, Hiroshi Watanabe
diffusion
More like this →
arXiv Computer Vision
Sep 4

WorldReward: Reward Modeling for Camera-Conditioned World Models

WorldReward introduces a vision‑language model–based reward system for camera‑conditioned world models, combining action consistency and visual quality evaluation. It processes paired videos by splitting them into action‑aligned chunks, structuring visual evidence, and aggregating decisions through voting. The model is trained on a large, reasoning‑augmented preference dataset and outperforms GPT‑5.5 on a human‑annotated benchmark, improving both action execution and visual quality when applied to RL post‑training.

By Yibin Wang, Zehan Wang, Junshu Tang, Zhimin Li, Yujie Zhou, Jiazi Bu, Pengyang Ling, Feng Han, Zhixiong Zhang, Long Xing, Shengyuan Ding, Ziang Li, Cheng Jin, Yuhang Zang, Jiaqi Wang, Tianyu Pang
llmsagentsreinforcement-learningmultimodalbenchmarks
More like this →
arXiv Computer Vision
Sep 2

CameraEditor: Camera-Controlled Image Editing via Video-Prior Sequential Modeling

CameraEditor is a new framework that transforms camera-controlled image editing into a temporal sequence prediction problem. By using video diffusion models, it incorporates a geometric perception module and dynamic reference routing to create precise visual references through dynamic panorama cropping. The method also inserts intermediate transition frames to handle large perspective shifts, maintaining content identity and spatial coherence, and is evaluated on a dataset of 5,760 instances with a benchmark of 462 test cases, achieving state‑of‑the‑art performance.

By Xin Shen, Chengyou Jia, Keshuo Xing, Zifeng Zhu, Changliang Xia, Bowen Ping, Zhuohang Dang, Hangwei Qian, Minnan Luo
diffusionroboticsbenchmarks
More like this →