AI RESEARCH
UniVL: Unified Vision-Language Embedding for Spatially Grounded Contextual Image Generation
arXiv CS.CV
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ArXi:2605.21611v1 Announce Type: new To address this task, we propose a framework in which the UniVL encoder, adapted from an optical-character-recognition-pretrained backbone, reads the unified condition optically and produces a UniVL embedding, fVIL, that fuses visual and semantic intent with spatial locations in a single token sequence. A two-stage pipeline first aligns UniVL with the VAE embedding space and then conditions a pretrained diffusion backbone entirely on UniVL embeddings, eliminating the standalone text encoder, such as T5.