Paint-Anything introduces a unified hex-prompt interface that allows users to specify any 24‑bit hex color for both image generation and editing. The method trains on a new Paint‑500K dataset created from real images with object grounding, perceptual color labeling, and editing‑pair synthesis, and supplements this with pure‑color anchors to address shadow‑induced color inaccuracies. Evaluated on the newly proposed Any Color Benchmark (ACBench), Paint‑Anything achieves significant improvements over the base FLUX.2‑4B model, boosting T2I and editing scores by 85.3 % and 28.3 % respectively, and outperforms competing methods on the CompColor metric.
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.
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.
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 ($L$).
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.
arXiv:2607. 08185v1 Announce Type: cross Abstract: Enhancing images to make them visually appealing is a persistent challenge in computer vision.
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.
Large-scale text-to-image models are attractive backbones for dense prediction because RGB generation pretraining learns rich semantic, structural, and geometric priors. Existing generative and editing approaches reuse these priors by casting dense prediction as target generation: annotations such as depth, normals, alpha mattes, masks, and heatmaps are encoded into an RGB-trained VAE latent space and decoded back as image-like targets.
VibeEdit is a new image‑editing tool that lets users draw spatial marks and add brief notes directly on an image to create a canvas instruction. These instructions guide the model to perform tasks such as adding, removing, replacing, or moving objects, as well as modifying attributes, without needing a separate text prompt. The system is trained on 1.55 million annotated edit pairs and uses a layer‑decoupled conditioning approach, achieving higher VLM rubric scores and PSNR on a benchmark that tests target selection among similar objects.
arXiv:2607. 13125v1 Announce Type: cross Abstract: We introduce Boogu-Image-0.
arXiv:2610.41210v1 Announce Type: new Abstract: Real-world image editing requires coordinating multiple visual transformations with content preservation and reference constraints, yet aggregate score...
arXiv:2607. 13125v2 Announce Type: replace-cross Abstract: We introduce Boogu-Image-0.
Swift-Image is a compact unified model that performs text-to-image generation, single-image editing, and multi-image editing using a 6B parameter DiT architecture. It employs a progressive training pipeline, parallel expert reinforcement learning, and multi-teacher distillation to balance diverse objectives, while a Prompt Enhancer decouples high-level reasoning from pixel-level rendering. After training, structural pruning and few-step distillation produce efficient 3B and accelerated variants that maintain near‑lossless performance and improve editing efficiency.