Lynx: Towards High-Fidelity Personalized Video Generation
Authors: name: Shen Sang, name: Tiancheng Zhi, name: Tianpei Gu, name: Jing Liu, name: Linjie Luo
Organization: ByteDance

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.