TY  - JOUR
TI  - Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet)
AU  - Zhang, L.
AU  - Rao, A.
AU  - Agrawala, M.
PY  - 2023
DA  - 2023/10//
JO  - IEEE/CVF International Conference on Computer Vision (ICCV 2023)
AB  - ControlNet introduces a neural network architecture that adds spatial conditioning inputs (pose skeletons, depth maps, edge maps, and other structural signals) to pretrained text-to-image diffusion models without requiring full retraining. The result is controllable image generation where the spatial layout and character pose can be specified precisely, a capability that has become foundational in AI character design workflows. With over 5,000 citations, ControlNet is a landmark of the generative image field and appears in virtually every node-based workflow taught in animation and concept art courses today.
KW  - generative-ai
KW  - image-generation
KW  - character-animation
KW  - production-practice
UR  - https://openaccess.thecvf.com/content/ICCV2023/html/Zhang_Adding_Conditional_Control_to_Text-to-Image_Diffusion_Models_ICCV_2023_paper.html
LA  - en
ER  -