An MLLM-guided architecture with a mixture of frequency experts and relational alignment loss achieves state-of-the-art all-in-one image restoration, outperforming prior methods by up to 1.35 dB on the CDD11 dataset.
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In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
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- background globally consistent 3D scenes that remain stable under large viewpoint changes. fundamentally an ill-posed problem: Simple texts or image inputs fail to provide a comprehensive representation of the entire 3D space. Consequently, inferring massive amounts of missing information for unseen areas while maintaining ge- ometric consistency remains a significant challenge. Deep generative models, particularly diffusion models [13,17,34,35,37], ad- dress this by leveraging strong 2D visual priors. How
- background language model to guide the evolution process. Importantly, it works on black-box generation models by requiring only image outputs. Finally, we evaluate PromptEvolver across multiple prompt inversion benchmarks and show that it consistently outperforms competing methods. Keywords:Prompt inversion·Text to image generation 1 Introduction Text-to-image (T2I) diffusion models [21,35,48] have transformed visual con- tent creation, enabling users to generate photorealistic images from natural- langua
- background Thisphysics-basedreference ˆImv v guaranteesglobalilluminationconsistencyacross views but lacks photorealistic high-frequency details (e.g. specularities, sky tex- tures), so we use it as a structural guidance signal for the generative stage. Generative Refinement via IC-Light.We refineˆImv v with IC-Light [48], a re- lighting diffusion model adapted from Stable Diffusion [25]. While IC-Light pro- duces photorealistic lighting effects, applying it independently per view breaks multi-view consist
- background Subsequent works further enhance controllability and semantic alignment, including Prompt-to-Prompt [11], DiffEdit [7], Imagic [18], Plug-and-Play Diffusion Features [43], and ControlNet [59]. More recent approaches explore richer instruction interfaces and multimodal reasoning, such as MGIE [9] and GenArtist [46], while subject-driven and compositional editing are studied in DreamBooth [35], Blended Diffusion [1], SDEdit [25], and image translation methods such as Detail Fusion GAN [ 20]. Comme
- background To validate the effectiveness of our proposed Neural Simulation in recovering real-world data distributions from simulation, we consider the following set of diverse comparative approaches: 1) Classical Simulation(Sim), denoting the canonical raw simulation pipeline without neural-driven refinement; 2) Baseline, a video-to-video generation model built on Stable Diffusion 1.5 [39] with temporal continuity post-processing [54]; 3) Zero-Shot, referring to the backbone model deployed without any sim
- background Several methods explicitly incorporate inpainting modules to hallucinate missing details in saturated re- gions [23,60,111]. However, when using limited-capacity generative models, the synthesized content often lacks realism or fine details. 2.3 Generative HDR Advancesingenerativemodeling,includingGANs[4,9,10,22,40,48-50,79,83,106] and diffusion models [3,16,31,34,39,67,74,88-90,96,102,105,107,108,112,113], have shown strong priors for image and video generation. Some approaches learn themapping
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citing papers explorer
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HairOrbit: Multi-view Aware 3D Hair Modeling from Single Portraits
HairOrbit leverages video generation priors and a neural orientation extractor to achieve state-of-the-art strand-level 3D hair reconstruction from single-view portraits in visible and invisible regions.
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ReplicateAnyScene: Zero-Shot Video-to-3D Composition via Textual-Visual-Spatial Alignment
ReplicateAnyScene performs fully automated zero-shot video-to-compositional-3D reconstruction by cascading alignments of generic priors from vision foundation models across textual, visual, and spatial dimensions.