arXiv Computer Vision
LEVI: Benchmarking Visual Identity Fidelity with Verifiable Low-Entropy Constructs
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WithEveryone: Unified Planning and Identity Grounding for Group Image Generation
Identity-preserving image generation becomes increasingly unreliable when a scene must contain many specified people. Beyond retaining each identity, the model must bind every reference to a distinct person and location, while training-time identity losses must establish correspondence among several noisy predicted faces.
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