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autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowFullScreen:!0,width:"100%"}),Object(v.jsxs)(j.a,{variant:"caption",align:"center",component:"div",children:["Open in ",Object(v.jsx)("a",{href:"https://youtu.be/Jvzltozpbpk",target:"_blank",children:"YouTube"})," or ",Object(v.jsx)("a",{href:"https://www.bilibili.com/video/BV1Z3411B7g7/",target:"_blank",children:"Bilibili"}),"."]}),Object(v.jsx)(j.a,{variant:"h6",gutterBottom:!0,children:"Abstract"}),Object(v.jsx)(j.a,{paragraph:!0,children:"We introduce a robust, real-time, high-resolution human video matting method that achieves new state-of-the-art performance. Our method is much lighter than previous approaches and can process 4K at 76 FPS and HD at 104 FPS on an Nvidia GTX 1080Ti GPU. Unlike most existing methods that perform video matting frame-by-frame as independent images, our method uses a recurrent architecture to exploit temporal information in videos and achieves significant improvements in temporal coherence and matting quality. Furthermore, we propose a novel training strategy that enforces our network on both matting and segmentation objectives. This significantly improves our model's robustness. Our method does not require any auxiliary inputs such as a trimap or a pre-captured background image, so it can be widely applied to existing human matting applications."}),Object(v.jsx)(j.a,{variant:"h6",gutterBottom:!0,children:"Download"}),Object(v.jsxs)(j.a,{paragraph:!0,children:["Our model is available in ",Object(v.jsx)("b",{children:"PyTorch"}),", ",Object(v.jsx)("b",{children:"TorchScript"}),", ",Object(v.jsx)("b",{children:"ONNX"}),", ",Object(v.jsx)("b",{children:"TensorFlow"}),", and ",Object(v.jsx)("b",{children:"TensorFlow.js"}),"."]}),Object(v.jsx)(y.a,{textAlign:"center",children:Object(v.jsx)(Q.a,{color:"primary",startIcon:Object(v.jsx)(H.a,{}),component:"a",href:"https://github.com/PeterL1n/RobustVideoMatting",target:"_blank",children:"Github"})}),Object(v.jsx)(j.a,{variant:"h6",gutterBottom:!0,children:"Supplementary"}),Object(v.jsx)(j.a,{gutterBottom:!0,children:"We show the recurrent states of our model in the following video. Our model learns to reconstruct the background and keep track of the motion history."}),Object(v.jsx)("iframe",{className:W()(e.video,e.video1Column),src:"https://www.youtube.com/embed/Ay-mGCEYEzM?rel=0&modestbranding=1&autohide=1&showinfo=0&enablejsapi=1",title:"YouTube video player",frameBorder:0,allow:"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture",allowFullScreen:!0,width:"100%"}),Object(v.jsxs)(j.a,{variant:"caption",align:"center",component:"div",children:["Open in ",Object(v.jsx)("a",{href:"https://youtu.be/Ay-mGCEYEzM",target:"_blank",children:"YouTube"})," or ",Object(v.jsx)("a",{href:"https://www.bilibili.com/video/BV19Q4y1h7xK/",target:"_blank",children:"Bilibili"}),"."]}),Object(v.jsx)(j.a,{variant:"h6",gutterBottom:!0,children:"Citation"}),Object(v.jsx)("code",{className:e.citation,children:"@misc{rvm,\n title={Robust High-Resolution Video Matting with Temporal Guidance}, \n author={Shanchuan Lin and Linjie Yang and Imran Saleemi and Soumyadip Sengupta},\n year={2021},\n eprint={2108.11515},\n archivePrefix={arXiv},\n primaryClass={cs.CV}\n}"})]})]})},z=Object(o.a)((function(e){return{appBar:{borderBottom:"1px solid #ddd"},toolbar:{minHeight:0},title:{flexGrow:1},tab:{textTransform:"none"}}})),X=function(){var e=z(),t=Object(m.h)(),n=Object(m.g)(),a=[{path:"/",title:"Project",component:Y},{path:"/demo",title:"Web Demo",component:C}];return Object(r.useEffect)((function(){window.scrollTo(0,0)}),[t.pathname]),Object(v.jsxs)(v.Fragment,{children:[Object(v.jsx)(s.a,{}),Object(v.jsx)(l.a,{className:e.appBar,elevation:0,color:"default",position:"sticky",children:Object(v.jsxs)(u.a,{className:e.toolbar,children:[Object(v.jsx)(j.a,{className:e.title,variant:"h6",children:"RVM"}),Object(v.jsx)(d.a,{value:t.pathname,onChange:function(e,t){return n.replace(t)},children:a.map((function(t){var n=t.path,r=t.title;return Object(v.jsx)(h.a,{className:e.tab,label:r,value:n},n)}))})]})}),Object(v.jsxs)(m.d,{children:[a.map((function(e){var t=e.path,n=e.component;return Object(v.jsx)(m.b,{path:t,component:n,exact:!0},t)})),Object(v.jsx)(m.b,{children:Object(v.jsx)(m.a,{to:"/"})})]})]})};c.a.render(Object(v.jsx)(a.a.StrictMode,{children:Object(v.jsx)(i.a,{children:Object(v.jsx)(X,{})})}),document.getElementById("root"))}},[[352,1,2]]]); //# sourceMappingURL=main.81634d48.chunk.js.map