Generative Models for Visual Content Editing and Creation

Generative AI now drives storyboarding, previs, and look-development, yet two gaps slow adoption: artists struggle with opaque tools, while ML engineers lack cinematic grammar. This half-day master class closes both gaps by pairing concise theory with hands-on, human-in-the-loop practice and built-in ethics. Through an Explain → Show → Do rhythm, each concept moves from a crisp technical snapshot to a live demo and a guided task. Team exercises turn peer critique into a rapid feedback loop, while questions of authorship, bias, and legal clearance surface at every step—embedding responsible practice into real production workflows. Live demos built on the CineVision pipeline transform a log-line into reference frames, shot lists, and colour-graded contact sheets, showcasing diffusion, LoRA, ControlNet, AnimateDiff, and IP-Adapter in action. Participants leave able to (i) explain how modern diffusion and multimodal generators work, (ii) customise tool-chains without ceding creative control, (iii) integrate AI assets into coherent, ethically sound sequences, and (iv) assess—and build—production-ready pipelines that enhance director–cinematographer collaboration.

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Publication Details

Published
2025-11-27
DOI
https://doi.org/10.1145/3757371.3763257
Citations
1
Primary Topic
Generative Adversarial Networks and Image Synthesis
Type
article
Field-Weighted Citation Impact
1.28
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article

Generative Models for Visual Content Editing and Creation

Xian Xu, G Han, Zheng Wei, Anyi Rao et al.
1 citations
Generative Adversarial Networks and Image Synthesis
1.28
article

Generative Models for Visual Content Editing and Creation

Xian Xu, G Han, Zheng Wei, Anyi Rao, Yuqing Liu
article en
1 citations

Abstract

Generative AI now drives storyboarding, previs, and look-development, yet two gaps slow adoption: artists struggle with opaque tools, while ML engineers lack cinematic grammar. This half-day master class closes both gaps by pairing concise theory with hands-on, human-in-the-loop practice and built-in ethics. Through an Explain → Show → Do rhythm, each concept moves from a crisp technical snapshot to a live demo and a guided task. Team exercises turn peer critique into a rapid feedback loop, while questions of authorship, bias, and legal clearance surface at every step—embedding responsible practice into real production workflows. Live demos built on the CineVision pipeline transform a log-line into reference frames, shot lists, and colour-graded contact sheets, showcasing diffusion, LoRA, ControlNet, AnimateDiff, and IP-Adapter in action. Participants leave able to (i) explain how modern diffusion and multimodal generators work, (ii) customise tool-chains without ceding creative control, (iii) integrate AI assets into coherent, ethically sound sequences, and (iv) assess—and build—production-ready pipelines that enhance director–cinematographer collaboration.

Palo Alto University (US), Hong Kong University of Science and Technology (HK), University of Hong Kong (HK), Stanford University (US)
Openalex Percentile: Top 14%
Generative Adversarial Networks and Image Synthesis
1.28
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Generative Models for Visual Content Editing and Creation — Xian Xu, G Han, et al. · (2025) | TGRS Research Map | TGRS