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Visual prompt engineering (VIPE) automatically modifies task images to improve video model reasoning performance, often more effective than text-based prompting or test-time scaling.
This paper introduces Visual Prompt Engineering (VIPE), a method that automatically modifies task images to improve video model performance, showing it can be more effective than text-based prompt engineering or test-time scaling.
The author launches a weekly Video Model Journal Club covering video generation, world models, physical reasoning, diffusion, flow matching, etc. The first in-person talk will be by Yilun Du on Embodied Reasoning with World Models.
LongCat released WBench, a benchmark for video world models that tests control, memory, instruction-following, and physical plausibility across 289 cases and 20 models, finding that no model excels in all dimensions, highlighting the gap between video quality and true world simulation.
A tweet thread reveals that Google's Omni is distinct from video models like Veo and Seedance, with DeepMind's Jeff Dean clarifying its unique input/output capabilities, described as a transformative AR filter for video.