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.
arXiv:2608. 19860v1 Announce Type: new Abstract: Single-shot exposure correction aims to map an arbitrarily degraded image---whether under-exposed, over-exposed, or a spatial mixture of both---to a well-exposed output from a single capture.
By Airin Akter Tania, Md Raihan Khan, Mohiuddin Ahmad
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.
By Ji Xie, Dewei Zhou, Xinyu Huang, Zhennan Chen, Xun Wang
arXiv:2607. 02131v1 Announce Type: cross Abstract: Restoring archival film remains a fundamentally challenging problem due to the absence of paired training data and the lack of standardized evaluation benchmarks.
By Miko{\l}aj Jastrz\k{e}bski, Dawid Glinkowski, Dawid Zieli\'nski, Daniel Borkowski, Wojciech Koz{\l}owski, Kamil Adamczewski
arXiv:2607.18227v2 Announce Type: replace
Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
By Dingyun Zhang, Lixue Gong, Wei Liu
arXiv:2606.13345v2 Announce Type: replace
Abstract: Existing 3D scene editing methods typically rely on per-scene optimization over explicit 3D representations or cascaded edit-and-reconstruct pipeli...
By Xinnan Zhu, Ruijie Xu, Jiayu Ying, Daoguo Dong, Jiachen Xu, Yuan Xie, Xin Tan
arXiv:2610.02044v1 Announce Type: new
Abstract: Recent advances in rectified flow-based image-to-3D generative models have enabled high-fidelity 3D asset generation. Building on this, a growing line...
By Tao Wu, Alexandra Gomez-Villa, Senmao Li, Yaxing Wang, Joost van de Weijer, Kai Wang
Camera intrinsics are vital for recovering 3D structure from 2D video. However, most 3D algorithms assume fixed intrinsics throughout a video, an assumption that often fails for real-world in-the-wild videos.
arXiv:2608.23549v1 Announce Type: new
Abstract: Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces...
By Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan
Pixel-space generative models bypass lossy latent compression, yet necessitate joint learning of global structure and fine-grained details in a high-dimensional space. Standard flow matching interpolates noise toward a fixed clean-image endpoint, leaving the spectral evolution to be learned implicitly.
MirrorDistill introduces an illumination‑aware latent distillation framework for low‑light image enhancement. It trains a lightweight student encoder‑decoder by aligning its intermediate features with clean‑domain targets generated by a teacher decoder, using feature mirroring and illumination‑aware weighting to emphasize underexposed regions. The method achieves state‑of‑the‑art performance on the LOL‑v2‑Real benchmark while maintaining the lowest computational complexity, and the code is released as open source.
By Farida Mohsen, Tala Zaim, Nurul Izni Rusli, Ali Al-Zawqari, Ali Safa, Samir Brahim Belhaouari
arXiv:2604. 10359v3 Announce Type: replace-cross Abstract: Low-light image enhancement (LLIE) aims to restore natural visibility, color fidelity, and structural detail under severe illumination degradation.
By Alexandru Brateanu, Tingting Mu, Codruta Ancuti, Cosmin Ancuti