arXiv:2606. 00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle.
By Kai Xu, Ellis Brown, Shrikar Madhu, Rob Fergus, He He, Saining Xie
PREEN (Preset Regression via an Exemplar-based Editor Network) performs color style transfer by predicting a 29‑parameter preset for a deterministic image editor, rather than generating a raster image. It uses a style‑transfer teacher to create pseudo‑presets, then trains a CLIP‑conditioned regressor refined with LoRA and a CIELAB loss. On the PST50 benchmark, PREEN outperforms baselines in pixel‑ and perceptual‑fidelity, runs in sub‑second on‑device latency, and offers a single inference‑time strength knob that smoothly transitions from default to vivid presets.
By Beomjin Ahn
arXiv:2608. 10798v1 Announce Type: cross Abstract: Most image colorization systems operate in $Lab$ space by predicting chroma ($ab$) while preserving an input-derived luminance channel ($L$).
By Swarnim Maheshwari, Syed Imam Ali, Vineeth N. Balasubramanian
ChromaGS is a real‑time, language‑guided method for color editing of animatable 3D Gaussian head avatars. It augments each Gaussian primitive with learned soft assignments to semantic regions and decomposes colors into region‑level base colors and Gaussian‑level residuals, enabling coherent color transfer while preserving fine details. A two‑stage language pipeline translates natural‑language instructions into target colors, supporting both absolute and relative adjustments without requiring retraining.
By Antonio Canela, Jordi S\`anchez-Riera
arXiv:2607. 08185v1 Announce Type: cross Abstract: Enhancing images to make them visually appealing is a persistent challenge in computer vision.
By David Serrano-Lozano, Luis Herranz, Michael S. Brown, Javier Vazquez-Corral
The paper introduces the Painting Change Map, a spatio‑temporal control mechanism that tracks normalized structural changes across a 1,000‑timestep continuum for dynamic video generation of paintings. By encoding localized visual differences into an RGB space, it provides fine‑grained guidance for both forward painting and reverse unpainting processes. Experiments show that this approach outperforms existing control baselines and matches or exceeds state‑of‑the‑art painting methods, especially under oracle conditions.
By Markus Pobitzer, Chang Liu, Niccol\`o Biondi, Nicu Sebe