ConsID-Gen: View-Consistent and Identity-Preserving Image-to-Video Generation

• URL: http://arxiv.org/abs/2602.10113v1
• Date: Tue, 10 Feb 2026 18:59:51 GMT
• Title: ConsID-Gen: View-Consistent and Identity-Preserving Image-to-Video Generation
• Authors: Mingyang Wu, Ashirbad Mishra, Soumik Dey, Shuo Xing, Naveen Ravipati, Hansi Wu, Binbin Li, Zhengzhong Tu,
• Abstract summary: Image-to-Video generation (I2V) animates a static image into a temporally coherent video sequence following textual instructions.<n>Existing I2V pipelines often suffer from appearance drift and geometric distortion.<n>We propose ConsID-Gen, a view-assisted I2V generation framework that augments the first frame with unposed auxiliary views.
• Score: 14.141157176094737
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Image-to-Video generation (I2V) animates a static image into a temporally coherent video sequence following textual instructions, yet preserving fine-grained object identity under changing viewpoints remains a persistent challenge. Unlike text-to-video models, existing I2V pipelines often suffer from appearance drift and geometric distortion, artifacts we attribute to the sparsity of single-view 2D observations and weak cross-modal alignment. Here we address this problem from both data and model perspectives. First, we curate ConsIDVid, a large-scale object-centric dataset built with a scalable pipeline for high-quality, temporally aligned videos, and establish ConsIDVid-Bench, where we present a novel benchmarking and evaluation framework for multi-view consistency using metrics sensitive to subtle geometric and appearance deviations. We further propose ConsID-Gen, a view-assisted I2V generation framework that augments the first frame with unposed auxiliary views and fuses semantic and structural cues via a dual-stream visual-geometric encoder as well as a text-visual connector, yielding unified conditioning for a Diffusion Transformer backbone. Experiments across ConsIDVid-Bench demonstrate that ConsID-Gen consistently outperforms in multiple metrics, with the best overall performance surpassing leading video generation models like Wan2.1 and HunyuanVideo, delivering superior identity fidelity and temporal coherence under challenging real-world scenarios. We will release our model and dataset at https://myangwu.github.io/ConsID-Gen.

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