Papers › Bring Clipart to Life
Bring Clipart to Life
Nanxuan Zhao, Shengqi Dang, Hexun Lin, Yang Shi, Nan Cao
The development of face editing has been boosted since the birth of StyleGAN. While previous works have explored different interactive methods, such as sketching and exemplar photos, they have been limited in terms of expressiveness and generality. In this paper, we propose a new interaction method by guiding the editing with abstract clipart, composed of a set of simple semantic parts, allowing users to control across face photos with simple clicks. However, this is a challenging task given the large domain gap between colorful face photos and abstract clipart with limited data. To solve this problem, we introduce a framework called ClipFaceShop built on top of StyleGAN. The key idea is to take advantage of W+ latent code encoded rich and disentangled visual features, and create a new lightweight selective feature adaptor to predict a modifiable path toward the target output photo. Since no pairwise labeled data exists for training, we design a set of losses to provide supervision signals for learning the modifiable path. Experimental results show that ClipFaceShop generates realistic and faithful face photos, sharing the same facial attributes as the reference clipart. We demonstrate that ClipFaceShop supports clipart in diverse styles, even in form of a free-hand sketch.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections