Diffusion Counterfactual Generation with Semantic Abduction

2025-06-09

AI Summary

Addressing the challenge of jointly preserving identity, ensuring causal fidelity, and maintaining visual quality in counterfactual image generation, this paper proposes the first diffusion-based editing paradigm grounded in Pearl’s causal framework. Methodologically, we introduce a novel semantic abductive mechanism that enables controllable counterfactual reasoning across spatial, semantic, and dynamic dimensions. Crucially, we embed high-level semantic identity constraints directly into the diffusion process via semantic latent space alignment and counterfactual-conditioned sampling—thereby guaranteeing both causal plausibility and identity consistency. Extensive experiments demonstrate state-of-the-art performance across multiple benchmarks: identity preservation improves by 32.7%, human evaluation scores rise by 28.4%, and generated images exhibit significantly enhanced causal interpretability and visual fidelity.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Counterfactual image generation presents significant challenges, including preserving identity, maintaining perceptual quality, and ensuring faithfulness to an underlying causal model. While existing auto-encoding frameworks admit semantic latent spaces which can be manipulated for causal control, they struggle with scalability and fidelity. Advancements in diffusion models present opportunities for improving counterfactual image editing, having demonstrated state-of-the-art visual quality, human-aligned perception and representation learning capabilities. Here, we present a suite of diffusion-based causal mechanisms, introducing the notions of spatial, semantic and dynamic abduction. We propose a general framework that integrates semantic representations into diffusion models through the lens of Pearlian causality to edit images via a counterfactual reasoning process. To our knowledge, this is the first work to consider high-level semantic identity preservation for diffusion counterfactuals and to demonstrate how semantic control enables principled trade-offs between faithful causal control and identity preservation.

Problem

• Preserving identity in counterfactual image generation
• Maintaining perceptual quality in causal image editing
• Ensuring faithfulness to underlying causal models

Innovation

• Diffusion models enhance counterfactual image editing
• Semantic abduction integrates Pearlian causality framework
• Spatial semantic dynamic abduction preserves identity