NeurIPS 2025 | UniLumos: 引入物理反馈的统一图像视频重打光框架，实现20倍加速的真实光影重塑!

Core Insights - The article discusses the advancements in image and video relighting technology, particularly focusing on the introduction of UniLumos, a unified framework that enhances physical consistency and computational efficiency in relighting tasks [3][37]. Group 1: Challenges in Existing Methods - Current methods based on diffusion models face two fundamental challenges: the lack of physical consistency and an inadequate evaluation system for relighting quality [11][12]. - Existing approaches often optimize in semantic latent spaces, leading to physical inconsistencies such as misaligned shadows, overexposed highlights, and incorrect occlusions [15][11]. Group 2: Introduction of UniLumos - UniLumos is introduced as a solution to the aforementioned challenges, providing a unified framework for image and video relighting that maintains scene structure and temporal consistency while achieving high-quality relighting [17][37]. - The framework incorporates geometric feedback from RGB space, such as depth and normal maps, to align lighting effects with scene structures, significantly improving physical consistency [4][22]. Group 3: Innovations and Methodology - Key innovations include a geometric feedback mechanism to enhance physical consistency and a structured six-dimensional lighting description for fine-grained control and evaluation of lighting effects [18][22]. - The training data set, LumosData, is constructed to extract high-quality relighting samples from real-world videos, facilitating the training of the model [20][21]. Group 4: Performance and Efficiency - UniLumos demonstrates superior performance across various metrics, achieving state-of-the-art results in visual fidelity, temporal consistency, and physical accuracy compared to baseline models [27][28]. - The framework achieves a 20-fold increase in inference speed while maintaining high-quality output, making it significantly more efficient than existing methods [33][38]. Group 5: Evaluation and Results - The LumosBench evaluation framework allows for automated and interpretable assessment of relighting accuracy across six dimensions, showcasing UniLumos's advantages in fine-grained control over lighting attributes [22][29]. - Qualitative results indicate that UniLumos produces more realistic lighting effects and maintains better temporal consistency compared to baseline methods [31][33].