Splatent recovers fine details for latent-space 3D Gaussian Splatting by applying multi-view attention in 2D rather than reconstructing in 3D space.
Flux already knows–activating subject-driven image generation without training
3 Pith papers cite this work. Polarity classification is still indexing.
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FreeGraftor performs subject-driven text-to-image generation without training by cross-image feature grafting via semantic matching, position-constrained attention fusion, and a noise initialization strategy that preserves reference geometry.
This review organizes literature on large multimodal models and object-centric vision into four themes—understanding, referring segmentation, editing, and generation—while summarizing paradigms, strategies, and challenges like instance permanence and consistent interaction.
citing papers explorer
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Splatent: Splatting Diffusion Latents for Novel View Synthesis
Splatent recovers fine details for latent-space 3D Gaussian Splatting by applying multi-view attention in 2D rather than reconstructing in 3D space.
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FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation
FreeGraftor performs subject-driven text-to-image generation without training by cross-image feature grafting via semantic matching, position-constrained attention fusion, and a noise initialization strategy that preserves reference geometry.
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LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation
This review organizes literature on large multimodal models and object-centric vision into four themes—understanding, referring segmentation, editing, and generation—while summarizing paradigms, strategies, and challenges like instance permanence and consistent interaction.