Keyframe-Anchored Identity Preservation for Sequential-Action Video Generation

• URL: http://arxiv.org/abs/2607.17985v1
• Date: Mon, 20 Jul 2026 14:16:49 GMT
• Title: Keyframe-Anchored Identity Preservation for Sequential-Action Video Generation
• Abstract summary: The IPVG26 challenge extends this framework from a single holistic prompt to a temporally structured specification.<n>This temporal structure presents a challenge not encountered in previous identity-preserving generation tasks.<n>We address this challenge with a training-free, three-stage pipeline framework.
• Score: 10.89407870318018
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Identity-preserving text-to-video generation aims to synthesize a video that accurately follows a textual description while maintaining the recognizability of a user-specified subject throughout. The IPVG26 challenge extends this framework from a single holistic prompt to a temporally structured specification. The model additionally receives a sequence of timestamped action captions and must render the subject performing these actions in the specified order. This temporal structure presents a challenge not encountered in previous identity-preserving generation tasks, as the subject must continuously perform a scripted sequence of distinct actions while maintaining a consistent identity. However, end-to-end video generators are prone to appearance drift as motion accumulates and the depicted actions change. We address this challenge with a training-free, three-stage pipeline framework. An action-aware prompt polishment stage first rewrites the inputs into image-generation prompts that specify the terminal state of each action. An identity-preserving generation stage then produces the keyframe sequence by conditioning each frame jointly on the reference identity and its predecessor, thereby decoupling time-invariant appearance from time-varying pose. Finally, an identity-aware inference enhancement stage synthesizes the intermediate segments using multi-reference guidance and identity-driven noise searching, both of which reinforce identity fidelity during sampling. Our method ranked third on the official Track 2 leaderboard, demonstrating competitive performance and strong generality.

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