Lynx: Towards High-Fidelity Personalized Video Generation
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.
68d0b1b98adc5cd018d15acf
Shen Sang
admin_assigned
2025-09-22T03:00:57.286Z
637d576c43fec4c21637a713
/avatars/9474afce8f834902e0c79de50c98f4fd.svg
Shen Sang
user
shensang
68d0b1b98adc5cd018d15ad0
Tiancheng Zhi
admin_assigned
2025-09-22T03:00:50.135Z
66dfbbcb6a706ea4ffdff8fd
/avatars/deda27ee93dae6f2eda960f650ad3dfb.svg
Tiancheng Zhi
user
tzhi-bytedance
68d0b1b98adc5cd018d15ad1
Tianpei Gu
admin_assigned
2025-09-22T03:00:42.511Z
6435ec65f3b08e267d9c9bbd
/avatars/7e846e0c91241898171aa35efd7264c0.svg
Tianpei Gu
user
gutianpei
68d0b1b98adc5cd018d15ad2
Jing Liu
68d0b1b98adc5cd018d15ad3
Linjie Luo
Lynx: Towards High-Fidelity Personalized Video Generation
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.
Lynx, a high-fidelity personalized video synthesis model, uses a Diffusion Transformer with ID-adapter and Ref-adapter to preserve identity and maintain video quality.
Diffusion Transformer DiT ID-adapter Perceiver Resampler ArcFace identity tokens Ref-adapter dense VAE cross-attention temporal coherence visual realism personalized video generation