GLADOS reconstructs 3D geometry from disjoint views by generating intermediate perspectives, performing robust coarse alignment that tolerates generative inconsistencies, and iteratively expanding context for consistency.
Clipscore: A reference-free evaluation metric for image captioning
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Auto-Rubric as Reward externalizes VLM preferences into structured rubrics and applies Rubric Policy Optimization to create more reliable binary rewards for multimodal generation, outperforming pairwise models on text-to-image and editing benchmarks.
Optimizing the noise schedule, preparing a balanced bucketed dataset, and aligning outputs with human preferences enables Playground v2.5 to reach state-of-the-art aesthetic quality across aspect ratios.
citing papers explorer
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Mind the Gap: Geometrically Accurate Generative Reconstruction from Disjoint Views
GLADOS reconstructs 3D geometry from disjoint views by generating intermediate perspectives, performing robust coarse alignment that tolerates generative inconsistencies, and iteratively expanding context for consistency.
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Auto-Rubric as Reward: From Implicit Preferences to Explicit Multimodal Generative Criteria
Auto-Rubric as Reward externalizes VLM preferences into structured rubrics and applies Rubric Policy Optimization to create more reliable binary rewards for multimodal generation, outperforming pairwise models on text-to-image and editing benchmarks.
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Playground v2.5: Three Insights towards Enhancing Aesthetic Quality in Text-to-Image Generation
Optimizing the noise schedule, preparing a balanced bucketed dataset, and aligning outputs with human preferences enables Playground v2.5 to reach state-of-the-art aesthetic quality across aspect ratios.