{"ID":2839363,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.15092","arxiv_id":"2511.15092","title":"Jointly Conditioned Diffusion Model for Multi-View Pose-Guided Person Image Synthesis","abstract":"Pose-guided human image generation is limited by incomplete textures from single reference views and the absence of explicit cross-view interaction. We present jointly conditioned diffusion model (JCDM), a jointly conditioned diffusion framework that exploits multi-view priors. The appearance prior module (APM) infers a holistic identity preserving prior from incomplete references, and the joint conditional injection (JCI) mechanism fuses multi-view cues and injects shared conditioning into the denoising backbone to align identity, color, and texture across poses. JCDM supports a variable number of reference views and integrates with standard diffusion backbones with minimal and targeted architectural modifications. Experiments demonstrate state of the art fidelity and cross-view consistency.","short_abstract":"Pose-guided human image generation is limited by incomplete textures from single reference views and the absence of explicit cross-view interaction. We present jointly conditioned diffusion model (JCDM), a jointly conditioned diffusion framework that exploits multi-view priors. The appearance prior module (APM) infers...","url_abs":"https://arxiv.org/abs/2511.15092","url_pdf":"https://arxiv.org/pdf/2511.15092v1","authors":"[\"Chengyu Xie\",\"Zhi Gong\",\"Junchi Ren\",\"Linkun Yu\",\"Si Shen\",\"Fei Shen\",\"Xiaoyu Du\"]","published":"2025-11-19T04:05:39Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Diffusion Model\"]","has_code":false}