GeoComposer: Geometry-Grounded Photographic Composition Instruction

arXiv:2609.26620 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/unsplash/datasets

Project page: https://geocomposer.github.io

Terminology

• Consistency — 50× in this paper · explained in 3 episodes
• Representation Learning — 17× in this paper · explained in 2 episodes
• Foundation Model — 16× in this paper · explained in 3 episodes
• Reinforcement Learning — 14× in this paper · explained in 4 episodes
• Ablation — 13× in this paper · explained in 1 episode
• Benchmark — 10× in this paper · explained in 2 episodes
• Transformer — 8× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Flow Matching — 6× in this paper · explained in 7 episodes
• Fidelity — 5× in this paper · explained in 2 episodes
• Ablation study — 4× in this paper · explained in 1 episode
• Accuracy — 4× in this paper · explained in 1 episode
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Ablation Studies — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Diffusion Model — 2× in this paper · explained in 8 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Generative Model — 2× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 2× in this paper · explained in 25 episodes
• ARK — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Alignment Methods — 1× in this paper · explained in 1 episode
• Commentary — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Diffusion Models — 1× in this paper · explained in 6 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Generative Modeling — 1× in this paper · explained in 3 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Reward Hacking — 1× in this paper · explained in 8 episodes
• Thinking mode — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Variational Autoencoder (VAE) — 1× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Qwen3-VL Technical Report
• What matters for Representation Alignment: Global Information or Spatial Structure?
• Unified Reward Model for Multimodal Understanding and Generation
• $\pi^3$: Permutation-Equivariant Visual Geometry Learning
• Emerging Properties in Unified Multimodal Pretraining
• AesFormer: Transform Everyday Photos into Beautiful Memories
• Emergent Outlier View Rejection in Visual Geometry Grounded Transformers
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• Step1X-Edit: A Practical Framework for General Image Editing
• DiffusionNFT: Online Diffusion Reinforcement with Forward Process
• Qwen-Image Technical Report
• Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers
• Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think

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