← 研踪 PaperTrailCVF · CVPR 2026:cvf:d3e17b91853d201a2b341b556b353d45 · 记录 #1
Stitch-a-Demo: Creating Video Demonstrations from Multistep Descriptions
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-02 00:31
摘要
When obtaining visual illustrations from text descriptions, today's methods take a description with a single text context--a caption, or an action description--and retrieve or generate the matching visual context. However, prior work does not permit visual illustration of multistep descriptions, e.g. a cooking recipe or a gardening instruction manual, and simply handling each step description in isolation would result in an incoherent demonstration. We propose Stitch-a-Demo, a novel retrieval-based method to assemble a video demonstration from a multistep description. The resulting video contains clips, possibly from different sources, that accurately reflect all the step descriptions, while being visually coherent. We formulate a training pipeline that creates large-scale weakly supervised data containing diverse procedures and injects hard negatives that promote both correctness and coherence. Validated on in-the-wild instructional videos, Stitch-a-Demo achieves state-of-the-art performance, with gains up to 29% as well as dramatic wins in a human preference study.
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记录 #1 · 2026 · 最新观测版本
Stitch-a-Demo: Creating Video Demonstrations from Multistep Descriptions
When obtaining visual illustrations from text descriptions, today's methods take a description with a single text context--a caption, or an action description--and retrieve or generate the matching visual context. However, prior work does not permit visual illustration of multistep descriptions, e.g. a cooking recipe or a gardening instruction manual, and simply handling each step description in isolation would result in an incoherent demonstration. We propose Stitch-a-Demo, a novel retrieval-based method to assemble a video demonstration from a multistep description. The resulting video contains clips, possibly from different sources, that accurately reflect all the step descriptions, while being visually coherent. We formulate a training pipeline that creates large-scale weakly supervised data containing diverse procedures and injects hard negatives that promote both correctness and coherence. Validated on in-the-wild instructional videos, Stitch-a-Demo achieves state-of-the-art performance, with gains up to 29% as well as dramatic wins in a human preference study.
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