Lynx: Towards High-Fidelity Personalized Video Generation
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909797418270720 |
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| author | Sang, Shen Zhi, Tiancheng Gu, Tianpei Liu, Jing Luo, Linjie |
| author_facet | Sang, Shen Zhi, Tiancheng Gu, Tianpei Liu, Jing Luo, Linjie |
| contents | We present Lynx, a high-fidelity model for personalized video synthesis from a single input image. Built on an open-source Diffusion Transformer (DiT) foundation model, Lynx introduces two lightweight adapters to ensure identity fidelity. The ID-adapter employs a Perceiver Resampler to convert ArcFace-derived facial embeddings into compact identity tokens for conditioning, while the Ref-adapter integrates dense VAE features from a frozen reference pathway, injecting fine-grained details across all transformer layers through cross-attention. These modules collectively enable robust identity preservation while maintaining temporal coherence and visual realism. Through evaluation on a curated benchmark of 40 subjects and 20 unbiased prompts, which yielded 800 test cases, Lynx has demonstrated superior face resemblance, competitive prompt following, and strong video quality, thereby advancing the state of personalized video generation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Lynx: Towards High-Fidelity Personalized Video Generation Sang, Shen Zhi, Tiancheng Gu, Tianpei Liu, Jing Luo, Linjie Computer Vision and Pattern Recognition We present Lynx, a high-fidelity model for personalized video synthesis from a single input image. Built on an open-source Diffusion Transformer (DiT) foundation model, Lynx introduces two lightweight adapters to ensure identity fidelity. The ID-adapter employs a Perceiver Resampler to convert ArcFace-derived facial embeddings into compact identity tokens for conditioning, while the Ref-adapter integrates dense VAE features from a frozen reference pathway, injecting fine-grained details across all transformer layers through cross-attention. These modules collectively enable robust identity preservation while maintaining temporal coherence and visual realism. Through evaluation on a curated benchmark of 40 subjects and 20 unbiased prompts, which yielded 800 test cases, Lynx has demonstrated superior face resemblance, competitive prompt following, and strong video quality, thereby advancing the state of personalized video generation. |
| title | Lynx: Towards High-Fidelity Personalized Video Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.15496 |