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Concat-ID: Towards Universal Identity-Preserving Video Synthesis

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arxiv 2503.14151 v3 pith:JGQBQVTT submitted 2025-03-18 cs.CV cs.AI

Concat-ID: Towards Universal Identity-Preserving Video Synthesis

Yong Zhong , Zhuoyi Yang , Jiayan Teng , Xiaotao Gu , Chongxuan Li This is my paper
classification cs.CV cs.AI
keywords concat-idvideogenerationidentity-preservingsynthesisadditionalalongapplications
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present Concat-ID, a unified framework for identity-preserving video generation. Concat-ID employs variational autoencoders to extract image features, which are then concatenated with video latents along the sequence dimension. It relies exclusively on inherent 3D self-attention mechanisms to incorporate them, eliminating the need for additional parameters or modules. A novel cross-video pairing strategy and a multi-stage training regimen are introduced to balance identity consistency and facial editability while enhancing video naturalness. Extensive experiments demonstrate Concat-ID's superiority over existing methods in both single and multi-identity generation, as well as its seamless scalability to multi-subject scenarios, including virtual try-on and background-controllable generation. Concat-ID establishes a new benchmark for identity-preserving video synthesis, providing a versatile and scalable solution for a wide range of applications.

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Cited by 4 Pith papers

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  1. Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation

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    Prompt Relay is an inference-time plug-and-play method that penalizes cross-attention to enforce temporal prompt alignment and reduce semantic entanglement in multi-event video generation.

  2. GroupVideo: Multi-Identity Customized Text-to-Video Generation

    cs.CV 2026-07 conditional novelty 6.0

    GroupVideo generates multi-person videos from reference photos plus text, using multimodal identity alignment and ID localization to keep each person's identity consistent.

  3. FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation

    cs.CV 2026-05 unverdicted novelty 6.0

    FaithfulFaces introduces a pose-faithful identity aligner with a shared dictionary and invariance constraint to maintain facial identity in text-to-video generation under large pose changes and occlusions.

  4. Keyframe-Anchored Identity Preservation for Sequential-Action Video Generation

    cs.CV 2026-07 conditional novelty 5.0

    A keyframe-anchored, training-free pipeline—terminal-state prompts, chained keyframe generation, and identity-aware sampling—ranks third on the IPVG26 Track 2 leaderboard.