SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models

A new automated pipeline has been introduced for generating domain-specific synthetic datasets using diffusion models, addressing the challenges posed by distribution shifts between pre-trained models and real-world applications. This three-stage framework synthesizes target objects within specific backgrounds, validates outputs through multi-modal assessments, and employs a user-preference classifier to enhance dataset quality.

The recent study titled 'CasTex: Cascaded Text-to-Texture Synthesis via Explicit Texture Maps and Physically-Based Shading' explores advancements in text-to-texture synthesis using diffusion models, aiming to generate realistic texture maps that perform well under various lighting conditions. This approach utilizes score distillation sampling to produce high-quality textures while addressing visual artifacts associated with existing methods.

A new approach called MMD Guidance has been proposed to enhance pre-trained diffusion models by addressing the issue of output deviation from user-specific target data, particularly in domain adaptation tasks where retraining is not feasible. This method utilizes Maximum Mean Discrepancy (MMD) to align generated samples with reference datasets without requiring additional training.