Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute
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arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866910191161704448 |
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| author | Kim, Daneul Zhang, Jingxu Jin, Wonjoon Cho, Sunghyun Dai, Qi Park, Jaesik Luo, Chong |
| author_facet | Kim, Daneul Zhang, Jingxu Jin, Wonjoon Cho, Sunghyun Dai, Qi Park, Jaesik Luo, Chong |
| contents | Subject-driven video generation (SDV-Gen) aims to produce videos of a specific subject by adapting a pretrained video model, enabling personalized and application-driven content creation. To achieve this goal, per-subject tuning methods require approximately 200 A100 GPU hours to generate a customized video, whereas zero-shot methods avoid per-subject tuning but typically rely on millions of subject-video pairs for the supervision, incurring massive network fine-tuning costs (10K-200K A100 GPU hours). We propose a data- and compute-efficient zero-shot SDV-Gen framework that avoids test-time per-subject tuning and the use of large-scale subject-video pairs. Our key idea decomposes SDV-Gen into (i) identity injection learned from subject-image pairs and (ii) motion-awareness preservation maintained by a small set of arbitrary videos. We optimize the two tasks with stochastic switching, using random reference-frame sampling and image-token dropout to prevent trivial first-frame copying. Our gradient analysis shows that the two objectives rapidly evolve toward nearly orthogonal update subspaces, explaining the stable optimization. Using CogVideoX-5B, we adapt a single model with 200K subject-image pairs and 4,000 arbitrary videos in 288 A100 GPU hours. This yields about 1% of compute compared to prior zero-shot baselines (i.e., 0.4% of VACE and 2.8% of Phantom) while using no subject-video pairs, yet remaining competitive in subject fidelity and motion quality. We show that the same recipe transfers to Wan 2.2-5B. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_17816 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute Kim, Daneul Zhang, Jingxu Jin, Wonjoon Cho, Sunghyun Dai, Qi Park, Jaesik Luo, Chong Computer Vision and Pattern Recognition Image and Video Processing Subject-driven video generation (SDV-Gen) aims to produce videos of a specific subject by adapting a pretrained video model, enabling personalized and application-driven content creation. To achieve this goal, per-subject tuning methods require approximately 200 A100 GPU hours to generate a customized video, whereas zero-shot methods avoid per-subject tuning but typically rely on millions of subject-video pairs for the supervision, incurring massive network fine-tuning costs (10K-200K A100 GPU hours). We propose a data- and compute-efficient zero-shot SDV-Gen framework that avoids test-time per-subject tuning and the use of large-scale subject-video pairs. Our key idea decomposes SDV-Gen into (i) identity injection learned from subject-image pairs and (ii) motion-awareness preservation maintained by a small set of arbitrary videos. We optimize the two tasks with stochastic switching, using random reference-frame sampling and image-token dropout to prevent trivial first-frame copying. Our gradient analysis shows that the two objectives rapidly evolve toward nearly orthogonal update subspaces, explaining the stable optimization. Using CogVideoX-5B, we adapt a single model with 200K subject-image pairs and 4,000 arbitrary videos in 288 A100 GPU hours. This yields about 1% of compute compared to prior zero-shot baselines (i.e., 0.4% of VACE and 2.8% of Phantom) while using no subject-video pairs, yet remaining competitive in subject fidelity and motion quality. We show that the same recipe transfers to Wan 2.2-5B. |
| title | Learning Zero-Shot Subject-Driven Video Generation Using 1% Compute |
| topic | Computer Vision and Pattern Recognition Image and Video Processing |
| url | https://arxiv.org/abs/2504.17816 |