From 7afcaa8219a6e507d006d3bc9dc8586a53076cb6 Mon Sep 17 00:00:00 2001 From: Samuel Teodoro <91779432+sateodoro@users.noreply.github.com> Date: Fri, 20 Dec 2024 16:16:34 +0900 Subject: [PATCH] Update index.html --- index.html | 96 +++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 85 insertions(+), 11 deletions(-) diff --git a/index.html b/index.html index 45992aa..7519d82 100644 --- a/index.html +++ b/index.html @@ -247,8 +247,76 @@

Method

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MIVE Dataset

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+ We also present our new MIVE Dataset specifically designed for multi-instance video editing tasks. + MIVE Dataset features 200 diverse videos sourced from the VIPSeg dataset. +

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+ We generated and summarized the source captions using LLaVa and Llama 3, respectively. + We then manually inserted tags in the source captions to establish instance-to-mask correspondence. + Finally, we generated the target edit captions using Llama 3. + We show a sample input video and source and target captions below. + The target instance captions are color-coded to match the color of the masks. +

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+ Source Caption: In a domestic setting, a person in a gray hoodie stands in front of + washing machine A and washing machine B against a blue wall, with a blue + recycling trash can to the left. +
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+ Source Video: +
+ Target Caption: In a domestic setting, an alien + stands in front of oven + and yellow washing machine against + a blue wall, with a blue recycling trash can to the left. +
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+ Masked Source Video: +
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MIVE Editing Results

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Comparison

Attention leakage examples are shown in green arrows while unfaithful editing examples are shown in red arrows. + The target instance captions are color-coded to match the color of the masks.

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BibTeX

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@article{teodoro2024mive,
+  title={MIVE: New Design and Benchmark for Multi-Instance Video Editing},
+  author={Samuel Teodoro and Agus Gunawan and Soo Ye Kim and Jihyong Oh and Munchurl Kim},
+  journal={arXiv preprint arXiv:2412.12877},
+  year={2024}
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