Overview
Field: Computer Vision Authors: Keming Wu, Zuhao Yang, Kaichen Zhang, Shizun Wang, Haowei Zhu, Sicong Leng, Zhongyu Yang, Qijie Wang, Sudong Wang, Ziting Wang, et al. Published: 2026-04-30 arXiv: 2604.28185
Summary
Recent visual generation models have made major progress in photorealism, typography, instruction following, and interactive editing, yet they still struggle with spatial reasoning, persistent state, long-horizon consistency, and causal understanding. The authors argue that the field should move beyond appearance synthesis toward intelligent visual generation: plausible visuals grounded in structure, dynamics, domain knowledge, and causal relations.
To frame this shift, the paper proposes a five-level taxonomy:
1. Atomic Generation 2. Conditional Generation 3. In-Context Generation 4. Agentic Generation 5. World-Modeling Generation
This progression describes an evolution from passive renderers to interactive, agentic, world-aware generators.
Key Technical Drivers
- Flow matching
- Unified understanding-and-generation models
- Improved visual representations
- Post-training
- Reward modeling
- Data curation
- Synthetic data distillation
- Sampling acceleration
On Evaluation
The authors note that current evaluations often overestimate progress by overemphasizing perceptual quality while overlooking structural, temporal, and causal flaws. By combining benchmark evaluations, in-the-wild stress tests, and expert-constrained case studies, the paper offers a capability-centric perspective for understanding, evaluating, and advancing next-generation intelligent visual generation systems.
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*Auto-collected on 2026-05-03.*