Spatial instruction following has become a crucial requirement for text-to-image (T2I) generation. A common challenge arises when directional expressions are interpreted under different frames of reference.
arXiv:2609.37709v1 Announce Type: cross Abstract: Recent multimodal image generation models can take multiple images and textual instructions as input, enabling reference-based generation guided not...
arXiv:2607. 00491v1 Announce Type: cross Abstract: Benchmarks for vision-language models (VLMs) mostly test observational spatial reasoning: models describe relations already visible in the input.
arXiv:2609.00663v1 Announce Type: new Abstract: Multimodal evaluations cannot say whether a vision-language model misread an image or misreasoned about it, because every existing method for separatin...
T2LSC-Bench is a new benchmark for evaluating localized semantic control in text-to-image generation, consisting of 50 seed subjects and 1,200 prompt cases per model, producing 7,160 images across six models. The benchmark measures Text-at-Anchor Accuracy, Semantic Subject Preservation, Semantic Leakage Rate, and Conditional Semantic Leakage Rate using a dual‑branch protocol that combines OCR‑VLM verification with structured VLM semantic judgments. Results show that while accurate text rendering remains high, semantic leakage can increase dramatically under stress‑test conditions, and anti‑leakage prompting can reduce leakage without harming rendering accuracy.
arXiv:2608.21762v1 Announce Type: cross Abstract: Vision-language models (VLMs) fail many detail-centric questions for a concrete reason: the answer is visible in the image, yet lost after the image...
arXiv:2509. 12046v2 Announce Type: replace-cross Abstract: Although autoregressive (AR) models have demonstrated remarkable success in image generation, extending these models to layout-conditioned generation remains challenging due to the sparse nature of layout conditions and the risk of feature entanglement.
arXiv:2607. 25537v1 Announce Type: cross Abstract: In the age of foundation models, a model is only as good as its prompt.
arXiv:2603. 08652v2 Announce Type: replace Abstract: Recent advancements in Unified Multimodal Models (UMMs) have significantly advanced text-to-image (T2I) generation, particularly through the integration of Chain-of-Thought (CoT) reasoning.
arXiv:2609.36219v1 Announce Type: new Abstract: Perspective taking is a fundamental component of spatial intelligence, requiring models interpret spatial relations from a specified viewpoint, such as...
The paper investigates why reasoning‑augmented text‑to‑image models like GoT‑R1 sometimes fail on compositional prompts. By separating the explicit textual plan from the decoder, the authors show that the decoder faithfully executes the plan while the planner often writes incorrect spatial relations, especially for phrasing‑dependent cues. Editing or replacing the plan improves image quality without retraining, demonstrating the viability of modular planner‑decoder architectures.
arXiv:2608.21832v1 Announce Type: new Abstract: Computer-use agents ground natural-language instructions in screenshots to locate interface elements, yet existing benchmarks do not isolate whether mo...
VTR-Bench is a new benchmark designed to evaluate how well video generation models render text within scenes. It includes 300 prompts across five real-world scenarios such as advertisements and scientific videos, and uses an automated pipeline with human alignment to assess text fidelity and scene/motion requirements. Experiments on 11 state‑of‑the‑art models show that even the best performer has a word error rate of 0.250, underscoring widespread challenges in visual text rendering.