MaGGIe: Masked Guided Gradual Human Instance Matting
Human matting is a foundation task in image and video processing, where human
foreground pixels are extracted from the input. Prior works either improve the
accuracy by additional guidance or improve the temporal consistency of a single
instance across frames. We propose a new framework MaGGIe, Masked Guided
Gradual Human Instance Matting, which predicts alpha mattes progressively for
each human instances while maintaining the computational cost, precision, and
consistency. Our method leverages modern architectures, including transformer
attention and sparse convolution, to output all instance mattes simultaneously
without exploding memory and latency. Although keeping constant inference costs
in the multiple-instance scenario, our framework achieves robust and versatile
performance on our proposed synthesized benchmarks. With the higher quality
image and video matting benchmarks, the novel multi-instance synthesis approach
from publicly available sources is introduced to increase the generalization of
models in real-world scenarios.
662a994f55fe93e3cdc2e47d
Chuong Huynh
claimed_verified
2024-04-26T07:42:57.693Z
63a4d196cde2b28f82a56bd9
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4d196cde2b28f82a56bd9/iqVFOtDteRMUScFGRcx0L.png
Chuong Huynh
user
chuonghm
662a994f55fe93e3cdc2e47e
Seoung Wug Oh
662a994f55fe93e3cdc2e47f
Abhinav Shrivastava
662a994f55fe93e3cdc2e480
Joon-Young Lee
claimed_verified
2024-04-26T07:42:59.239Z
64b76c7990b38df83381824b
/avatars/0a5bec2ea480fb3f43c4b24d59c50e81.svg
Joon-Young Lee
user
joonyounglee
MaGGIe: Masked Guided Gradual Human Instance Matting
Human matting is a foundation task in image and video processing, where human
foreground pixels are extracted from the input. Prior works either improve the
accuracy by additional guidance or improve the temporal consistency of a single
instance across frames. We propose a new framework MaGGIe, Masked Guided
Gradual Human Instance Matting, which predicts alpha mattes progressively for
each human instances while maintaining the computational cost, precision, and
consistency. Our method leverages modern architectures, including transformer
attention and sparse convolution, to output all instance mattes simultaneously
without exploding memory and latency. Although keeping constant inference costs
in the multiple-instance scenario, our framework achieves robust and versatile
performance on our proposed synthesized benchmarks. With the higher quality
image and video matting benchmarks, the novel multi-instance synthesis approach
from publicly available sources is introduced to increase the generalization of
models in real-world scenarios.
MaGGIe, a new framework for human instance matting, uses transformer attention and sparse convolution to maintain computational efficiency while achieving robust performance across multiple instances.
transformer attention sparse convolution multi-instance