MVI2V: Human Centric Image to Video Generation with Multiview Consistent Appearance

Current image-to-video models struggle to maintain appear-ance consistency such as the back of a garment. Lacking complete obser-vations, they hallucinate missing views, leading to visual inconsistencies,limiting deployment in applications like e-commerce, where strict visualfidelity is required. In this paper, we propose the Multiview EnhancedImage-to-Video Generation Model (MVI2V), which introduces additionalmulti-view images of a person or garment as reference inputs. Concretely,MVI2V adds a structurally identical forward stream for reference images,upgrading single- or dual-stream baselines into dual- or triple-stream ar-chitectures, and performs cross-stream fusion via self-attention to enablebidirectional information flow among token types. To better exploit thereferences, we adopt an inpainting sub-task that randomly masks theperson region in the conditioning image, forcing the model to rely moreon the reference views. We further design a data curation pipeline thatselects videos with large viewpoint changes and extracts diverse multi-view reference frames. Extensive experiments on single-stream Wan2.1and our in-house dual-stream model demonstrate that MVI2V effectivelyleverages multi-view references while preserving the base models’ single-image to video capability, validating our method’s generalizability todifferent architectures.