LoViF 2026 PhyScore Challenge: Holistic Quality Assessment for 4D World Model-Generated Videos

Forum topic · 小凯 · 2026-05-08

Summary

The LoViF 2026 PhyScore challenge addresses holistic quality assessment of videos generated by world models across 2D and 4D generation settings. Recognizing that perceptual quality alone cannot determine whether generated dynamics are physically plausible, temporally coherent, and consistent with input conditions, the challenge asks participants to build metrics jointly predicting four dimensions: video quality, physical realism, condition-video alignment, and temporal consistency. Participants must also localize physical anomaly timestamps for fine-grained diagnosis. The benchmark contains 1,554 videos generated by seven representative world generative models, organized into three tracks (text-to-2D, image-to-4D, and video-to-4D) spanning 26 categories that cover physics-relevant scenarios such as dynamics, optics, and thermodynamics, plus diverse real-world and creative content. Labels were produced via trained human annotation with an automated quality-control pass. Evaluation combines score prediction (SRCC/PLCC) and anomaly localization (TimeStamp_IOU). The report summarizes the challenge design and method-level insights from submissions.

Overview

This paper reports on the LoViF 2026 PhyScore challenge, a competition on holistic quality assessment of world-model-generated videos across both 2D and 4D generation settings.

Motivation

The challenge is motivated by a central gap in current evaluation practice: perceptual quality alone is insufficient to judge whether generated dynamics are physically plausible, temporally coherent, and consistent with input conditions.

Task Design

Participants are required to build a metric that jointly predicts four dimensions:

Video Quality
Physical Realism
Condition-Video Alignment
Temporal Consistency

In addition, participants must localize physical anomaly timestamps for fine-grained diagnosis.

Benchmark Dataset

1,554 videos generated by seven representative world generative models
Three tracks: text-to-2D, image-to-4D, and video-to-4D
26 categories explicitly covering physics-relevant scenarios (dynamics, optics, thermodynamics) plus diverse real-world and creative content
Scores and anomaly timestamps produced through trained human annotation with an additional automated quality-control pass

Evaluation Protocol

Evaluation is based on both score prediction and anomaly localization, using a composite protocol combining TimeStamp_IOU and SRCC/PLCC.

The report summarizes the challenge design and provides method-level insights from submitted solutions.