DiffuMatting：利用抠图级标注合成任意对象
DiffuMatting: Synthesizing Arbitrary Objects with Matting-level Annotation
AI总结：
提出DiffuMatting，利用扩散模型在绿幕上合成任意对象并同时生成抠图标注，通过Green100K数据集和两种损失实现高精度抠图，作为数据生成器显著降低MSE误差。
摘要：
由于获取高精度或抠图标注的难度和劳动密集性，公开可用的高精度标注数据量有限。为应对这一挑战，我们提出了DiffuMatting，它继承了扩散模型强大的万物生成能力，并赋予了“万物抠图”的能力。我们的DiffuMatting可以：1）充当高精度标注的万物抠图工厂；2）与社区LoRA或各种条件控制方法良好兼容，实现社区友好的艺术设计和可控生成。具体而言，受绿幕抠图的启发，我们旨在教会扩散模型在固定的绿幕画布上绘画。为此，我们收集了一个大规模绿幕数据集（Green100K）作为DiffuMatting的训练数据集。其次，提出了绿背景控制损失，以保持画板为纯绿色，从而区分前景和背景。为确保合成对象具有更多边缘细节，提出了过渡边界损失的细节增强，作为生成具有更复杂边缘结构的对象的指导。为了同时生成对象及其抠图标注，我们在VAE解码器的潜在空间中构建了一个抠图头，用于去除绿色。我们的DiffuMatting展示了几个潜在应用（例如，抠图数据生成器、社区友好的艺术设计和可控生成）。作为抠图数据生成器，DiffuMatting合成了通用对象和肖像抠图集，在通用对象抠图任务中有效降低了15.4%的相对MSE误差，在肖像抠图任务中降低了11.4%。数据集已在我们的项目页面发布：\url{https://diffumatting.github.io}。
英文摘要：
Due to the difficulty and labor-consuming nature of getting highly accurate or matting annotations, there only exists a limited amount of highly accurate labels available to the public. To tackle this challenge, we propose a DiffuMatting which inherits the strong Everything generation ability of diffusion and endows the power of "matting anything". Our DiffuMatting can 1). act as an anything matting factory with high accurate annotations 2). be well-compatible with community LoRAs or various conditional control approaches to achieve the community-friendly art design and controllable generation. Specifically, inspired by green-screen-matting, we aim to teach the diffusion model to paint on a fixed green screen canvas. To this end, a large-scale greenscreen dataset (Green100K) is collected as a training dataset for DiffuMatting. Secondly, a green background control loss is proposed to keep the drawing board as a pure green color to distinguish the foreground and background. To ensure the synthesized object has more edge details, a detailed-enhancement of transition boundary loss is proposed as a guideline to generate objects with more complicated edge structures. Aiming to simultaneously generate the object and its matting annotation, we build a matting head to make a green color removal in the latent space of the VAE decoder. Our DiffuMatting shows several potential applications (e.g., matting-data generator, community-friendly art design and controllable generation). As a matting-data generator, DiffuMatting synthesizes general object and portrait matting sets, effectively reducing the relative MSE error by 15.4% in General Object Matting and 11.4% in Portrait Matting tasks. The dataset is released in our project page at \url{https://diffumatting.github.io}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
