Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights
Juanxi Tian, Siyuan Li, Conghui He, Lijun Wu, Cheng Tan
2025-12-02
Summary
This paper investigates how well current AI models that handle both text and images actually *understand* how things change over time, rather than just creating pretty pictures based on keywords.
What's the problem?
Existing AI models are really good at creating images from text descriptions, but they're trained and tested on single images. This means they learn to match words to static pictures and combine elements, but they don't understand how events unfold or how things move and change in a realistic way. They essentially memorize patterns instead of truly grasping cause and effect.
What's the solution?
The researchers created a new benchmark called 'Envision' which requires AI to generate a *series* of images showing a process happening over four steps. These scenarios are based on real-world knowledge from science and humanities. They also developed a new scoring system, 'Envision-Score', that checks not just if the images look good, but also if they make sense as a sequence, follow the laws of physics, and show a consistent story. They tested 15 different AI models with this benchmark.
Why it matters?
The results show that while some models can create visually appealing images, they often lack a real understanding of how the world works. Models that can handle both text and images do better at understanding the sequence of events, but even the best ones still struggle with creating consistent, realistic changes over time. This highlights the need for AI training that focuses on dynamic processes and causal relationships, rather than just static image generation, to truly build AI that understands the world.
Abstract
Current multimodal models aim to transcend the limitations of single-modality representations by unifying understanding and generation, often using text-to-image (T2I) tasks to calibrate semantic consistency. However, their reliance on static, single-image generation in training and evaluation leads to overfitting to static pattern matching and semantic fusion, while fundamentally hindering their ability to model dynamic processes that unfold over time. To address these constraints, we propose Envision-a causal event progression benchmark for chained text-to-multi-image generation. Grounded in world knowledge and structured by spatiotemporal causality, it reorganizes existing evaluation dimensions and includes 1,000 four-stage prompts spanning six scientific and humanities domains. To transition evaluation from single images to sequential frames and assess whether models truly internalize world knowledge while adhering to causal-temporal constraints, we introduce Envision-Score, a holistic metric integrating multi-dimensional consistency, physicality, and aesthetics. Comprehensive evaluation of 15 models (10 specialized T2I models, 5 unified models) uncovers: specialized T2I models demonstrate proficiency in aesthetic rendering yet lack intrinsic world knowledge. Unified multimodal models bridge this gap, consistently outperforming specialized counterparts in causal narrative coherence. However, even these unified architectures remain subordinate to closed-source models and struggle to overcome the core challenge of spatiotemporal consistency. This demonstrates that a focus on causally-isolated single images impedes multi-frame reasoning and generation, promoting static pattern matching over dynamic world modeling-ultimately limiting world knowledge internalization, generation.