Chunjiang Liu

CMU MS | AI Agent & 3D Vision

[TonyC @TonyC56348978]

Excited to share MemoBench, our ECCV 2026 benchmark for evaluating visual memory in dynamic world modeling.

When objects disappear from view, can video/world models recover their updated states when they reappear?

Paper: https://t.co/j6lVBAsZd9
Project/code/data/leaderboard: https://t.co/f72bU1XpLY

#ECCV2026 #WorldModels #VideoGeneration

[Moonlake @moonlake]

https://t.co/vHXbGcwESD

[Zhangchen Xu @zhangchen_xu]

🚀 We built Visual Aesthetic Benchmark (VAB). Arena is alive here: https://t.co/GKSvoJ5jAk

Aesthetic judgment is one of the hardest ceilings for AI to crack right now. Not generating images, but truly understanding what “looks good.”

We hand-curated 400 sets of artist works (fine art, photography, and illustration), featuring 2000+ hours of brand-new commissioned data created specifically for this benchmark — all grounded in 13K+ domain expert judgments across 7 core aesthetic dimensions (composition, lighting, technique, expression…) to ensure rigorous evaluation in highly subjective domains.

We asked 20+ frontier AI models to judge visual aesthetics (fine art, photography, illustration) against domain experts.
Frontier models are really not good at it yet.
Best model, Claude Sonnet 4.6 hit 26.5%. Human experts: 68.9%.

> Blog: https://t.co/XvEgwkmRqr
> Leaderboard: https://t.co/BEsHCgTmjC