The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
It addresses the problem of making disentangled representation learning more applicable and robust for researchers and practitioners in fields like computer vision and AI, but it is incremental as it summarizes existing workshop contributions rather than presenting new research.
The paper reviews a workshop that tackled the gap between theoretical disentangled representation learning and its practical applications, focusing on controllable generation and evaluating methods in realistic scenarios like autonomous driving and EEG analysis, with 9 accepted papers covering topics such as diffusion models and 3D-aware disentanglement.
This paper reviews the 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real), held in conjunction with ICCV 2025. The workshop aimed to bridge the gap between the theoretical promise of Disentangled Representation Learning (DRL) and its application in realistic scenarios, moving beyond synthetic benchmarks. DRL4Real focused on evaluating DRL methods in practical applications such as controllable generation, exploring advancements in model robustness, interpretability, and generalization. The workshop accepted 9 papers covering a broad range of topics, including the integration of novel inductive biases (e.g., language), the application of diffusion models to DRL, 3D-aware disentanglement, and the expansion of DRL into specialized domains like autonomous driving and EEG analysis. This summary details the workshop's objectives, the themes of the accepted papers, and provides an overview of the methodologies proposed by the authors.