PoseGen: In-Context LoRA Finetuning for Pose-Controllable Long Human Video Generation

• URL: http://arxiv.org/abs/2508.05091v1
• Date: Thu, 07 Aug 2025 07:19:02 GMT
• Title: PoseGen: In-Context LoRA Finetuning for Pose-Controllable Long Human Video Generation
• Authors: Jingxuan He, Busheng Su, Finn Wong,
• Abstract summary: We introduce PoseGen, a novel framework that generates arbitrarily long videos of a specific subject from a single reference image and a driving pose sequence.<n>Our core innovation is an in-context LoRA finetuning strategy that injects subject appearance at the token level for identity preservation.<n>We show that PoseGen significantly outperforms state-of-the-art methods in identity fidelity, pose accuracy, and its unique ability to produce coherent, artifact-free videos of unlimited duration.
• Score: 4.417342791754854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generating long, temporally coherent videos with precise control over subject identity and motion is a formidable challenge for current diffusion models, which often suffer from identity drift and are limited to short clips. We introduce PoseGen, a novel framework that generates arbitrarily long videos of a specific subject from a single reference image and a driving pose sequence. Our core innovation is an in-context LoRA finetuning strategy that injects subject appearance at the token level for identity preservation, while simultaneously conditioning on pose information at the channel level for fine-grained motion control. To overcome duration limits, PoseGen pioneers an interleaved segment generation method that seamlessly stitches video clips together, using a shared KV cache mechanism and a specialized transition process to ensure background consistency and temporal smoothness. Trained on a remarkably small 33-hour video dataset, extensive experiments show that PoseGen significantly outperforms state-of-the-art methods in identity fidelity, pose accuracy, and its unique ability to produce coherent, artifact-free videos of unlimited duration.

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