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.
Authors
- Xian Xu
- G Han (ORCID: https://orcid.org/0009-0009-1890-5362)
- Zheng Wei (ORCID: https://orcid.org/0000-0001-7444-2547)
- Anyi Rao (ORCID: https://orcid.org/0000-0003-1004-7753)
- Yuqing Liu
Institutions
- Palo Alto University (US)
- Hong Kong University of Science and Technology (HK)
- University of Hong Kong (HK)
- Stanford University (US)
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