TY - JOUR
T1 - Write-A-video
T2 - Computational video montage from themed text
AU - Wang, Miao
AU - Yang, Guo Wei
AU - Hu, Shi Min
AU - Yau, Shing Tung
AU - Shamir, Ariel
N1 - Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/11
Y1 - 2019/11
N2 - We present Write-A-Video, a tool for the creation of video montage using mostly text-editing. Given an input themed text and a related video repository either from online websites or personal albums, the tool allows novice users to generate a video montage much more easily than current video editing tools. The resulting video illustrates the given narrative, provides diverse visual content, and follows cinematographic guidelines. The process involves three simple steps: (1) the user provides input, mostly in the form of editing the text, (2) the tool automatically searches for semantically matching candidate shots from the video repository, and (3) an optimization method assembles the video montage. Visual-semantic matching between segmented text and shots is performed by cascaded keyword matching and visual-semantic embedding, that have better accuracy than alternative solutions. The video assembly is formulated as a hybrid optimization problem over a graph of shots, considering temporal constraints, cinematography metrics such as camera movement and tone, and user-specified cinematography idioms. Using our system, users without video editing experience are able to generate appealing videos.
AB - We present Write-A-Video, a tool for the creation of video montage using mostly text-editing. Given an input themed text and a related video repository either from online websites or personal albums, the tool allows novice users to generate a video montage much more easily than current video editing tools. The resulting video illustrates the given narrative, provides diverse visual content, and follows cinematographic guidelines. The process involves three simple steps: (1) the user provides input, mostly in the form of editing the text, (2) the tool automatically searches for semantically matching candidate shots from the video repository, and (3) an optimization method assembles the video montage. Visual-semantic matching between segmented text and shots is performed by cascaded keyword matching and visual-semantic embedding, that have better accuracy than alternative solutions. The video assembly is formulated as a hybrid optimization problem over a graph of shots, considering temporal constraints, cinematography metrics such as camera movement and tone, and user-specified cinematography idioms. Using our system, users without video editing experience are able to generate appealing videos.
KW - Computational cinematography
KW - User interaction
KW - Video montage
UR - https://www.scopus.com/pages/publications/85078909566
U2 - 10.1145/3355089.3356520
DO - 10.1145/3355089.3356520
M3 - 文章
AN - SCOPUS:85078909566
SN - 0730-0301
VL - 38
JO - ACM Transactions on Graphics
JF - ACM Transactions on Graphics
IS - 6
M1 - 177
ER -