Delving into Asymmetric Information Dynamics for High-Fidelity Virtual Try-On

arXiv:2609.25881 · cs.CV · Submitted 2026-09-22 · Read on arXiv

cs.CV

Submitted: 2026-09-22

Updated: 2026-09-22

Terminology

• Fidelity — 41× in this paper · explained in 2 episodes
• Transformer — 10× in this paper · explained in 2 episodes
• Attention Entropy — 8× in this paper · explained in 2 episodes
• Diffusion Model — 7× in this paper · explained in 8 episodes
• Conditional Attention Entropy — 6× in this paper
• Diffusion Models — 6× in this paper · explained in 6 episodes
• Injected Information Flux — 6× in this paper
• KV cache — 6× in this paper · explained in 3 episodes
• Benchmark — 5× in this paper · explained in 2 episodes
• Unidirectional Information Flow — 5× in this paper
• Decoupled Timestep Modulation — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Generalization — 3× in this paper · explained in 13 episodes
• Joint Attention — 3× in this paper · explained in 2 episodes
• Latent Diffusion Model — 3× in this paper · explained in 2 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• CVF International Conference on Computer Vision — 2× in this paper
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Embeddings — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• European Conference on Computer Vision — 1× in this paper
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Flow Matching — 1× in this paper · explained in 8 episodes
• Generative Model — 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
• Latent Space — 1× in this paper · explained in 7 episodes
• Nash Equilibrium — 1× in this paper · explained in 2 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 2 episodes
• Rectified Flow — 1× in this paper · explained in 3 episodes
• Semantic Alignment — 1× in this paper · explained in 3 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes

Sources

• Improving Diffusion Models for Authentic Virtual Try-on in the Wild
• Prompt-to-Prompt Image Editing with Cross Attention Control
• SpotEdit: Selective Region Editing in Diffusion Transformers
• FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space
• OmniGen: Unified Image Generation
• OOTDiffusion: Outfitting Fusion based Latent Diffusion for Controllable Virtual Try-on

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