Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights
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.
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Juanxi Tian
claimed_verified
2025-12-02T08:40:43.760Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/670880950e79a8b46f7ff9dd/hA1TLhwlQblkFsq8wLrkB.jpeg
Juanxi Tian
Juanxi
user
Juanxi
692e5c0537312eaa83fd87b9
Siyuan Li
692e5c0537312eaa83fd87ba
Conghui He
692e5c0537312eaa83fd87bb
Lijun Wu
692e5c0537312eaa83fd87bc
Cheng Tan
claimed_verified
2025-12-02T08:40:41.755Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/64be296a46cc3cdfbb057f7e/jSHeNY2AcPifCZzJyFhr4.jpeg
Cheng Tan
chengtan9907
user
chengtan9907
Envision: Benchmarking Unified Understanding & Generation for Causal World Process Insights
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.
A benchmark for chained text-to-multi-image generation assesses models' ability to model dynamic causal processes and world knowledge, revealing that unified multimodal models outperform specialized ones but still struggle with spatiotemporal consistency.
multimodal models text-to-image (T2I) causal event progression spatiotemporal causality Envision-a Envision-Score multi-dimensional consistency physicality aesthetics causal narrative coherence spatiotemporal consistency multi-frame reasoning dynamic world modeling