聚焦可控制生成的解耦表征学习，推动理论向真实场景落地。
The 1st International Workshop on Disentangled Representation Learning for Controllable Generation (DRL4Real): Methods and Results
- 整合语言先验等新归纳偏置，提升模型可解释性。
- 9篇论文覆盖扩散模型、3D感知与自动驾驶等实际应用。
- 适合关注生成模型可控性与现实应用的研究者。
本文综述了2025年国际计算机视觉大会（ICCV 2025）期间举办的首届「解耦表征学习用于可控生成国际研讨会」（DRL4Real）。该研讨会旨在弥合解耦表征学习（DRL）理论潜力与真实应用场景之间的差距，突破传统合成基准的局限。会议聚焦于可控生成中的实际问题，探讨模型鲁棒性、可解释性与泛化能力的提升。共收录9篇论文，涵盖语言先验等新型归纳偏置引入、扩散模型在DRL中的应用、3D感知解耦以及自动驾驶、脑电图（EEG）分析等专用领域的拓展。本文详述了研讨会目标、入选论文主题，并概述了作者提出的方法论。
原文摘要 · Abstract (English)
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.
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