DiffuMatting: Synthesizing Arbitrary Objects with Matting-level
  Annotation
Xiaobin Hu Xu Peng Donghao Luo Xiaozhong Ji Jinlong Peng Zhengkai Jiang Jiangning Zhang Taisong Jin Chengjie Wang Rongrong Ji
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.