V-Warper: Appearance-Consistent Video Diffusion Personalization via Value Warping

• URL: http://arxiv.org/abs/2512.12375v1
• Date: Sat, 13 Dec 2025 16:05:52 GMT
• Title: V-Warper: Appearance-Consistent Video Diffusion Personalization via Value Warping
• Authors: Hyunkoo Lee, Wooseok Jang, Jini Yang, Taehwan Kim, Sangoh Kim, Sangwon Jung, Seungryong Kim,
• Abstract summary: V-Warper is a training-free coarse-to-fine personalization framework for transformer-based video diffusion models.<n>It enhances fine-grained identity fidelity without requiring any additional video training.<n>It significantly improves appearance fidelity while preserving prompt alignment and motion dynamics.
• Score: 42.87624197744494
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Video personalization aims to generate videos that faithfully reflect a user-provided subject while following a text prompt. However, existing approaches often rely on heavy video-based finetuning or large-scale video datasets, which impose substantial computational cost and are difficult to scale. Furthermore, they still struggle to maintain fine-grained appearance consistency across frames. To address these limitations, we introduce V-Warper, a training-free coarse-to-fine personalization framework for transformer-based video diffusion models. The framework enhances fine-grained identity fidelity without requiring any additional video training. (1) A lightweight coarse appearance adaptation stage leverages only a small set of reference images, which are already required for the task. This step encodes global subject identity through image-only LoRA and subject-embedding adaptation. (2) A inference-time fine appearance injection stage refines visual fidelity by computing semantic correspondences from RoPE-free mid-layer query--key features. These correspondences guide the warping of appearance-rich value representations into semantically aligned regions of the generation process, with masking ensuring spatial reliability. V-Warper significantly improves appearance fidelity while preserving prompt alignment and motion dynamics, and it achieves these gains efficiently without large-scale video finetuning.

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