Proteus-ID: ID-Consistent and Motion-Coherent Video Customization

Guiyu Zhang1, Chen Shi1, Zijian Jiang1, Xunzhi Xiang2,
Jingjing Qian1, Shaoshuai Shi3, Li Jiang1†
1The Chinese University of Hong Kong, Shenzhen, 2Nanjing University, 3Voyager Research, Didi Chuxing

Abstract

Video identity customization seeks to synthesize realistic, temporally coherent videos of a specific subject, given a single reference image and a text prompt. This task presents two core challenges: (1) maintaining identity consistency while aligning with the described appearance and actions, and (2) generating natural, fluid motion without unrealistic stiffness. To address these challenges, we introduce Proteus-ID, a novel diffusion-based framework for identity-consistent and motion-coherent video customization. First, we propose a Multimodal Identity Fusion (MIF) module that unifies visual and textual cues into a joint identity representation using a Q-Former, providing coherent guidance to the diffusion model and eliminating modality imbalance. Second, we present a Time-Aware Identity Injection (TAII) mechanism that dynamically modulates identity conditioning across denoising steps, improving fine-detail reconstruction. Third, we propose Adaptive Motion Learning (AML), a self-supervised strategy that reweights the training loss based on optical-flow-derived motion heatmaps, enhancing motion realism without requiring additional inputs. To support this task, we construct Proteus-Bench, a high-quality dataset comprising 200K curated clips for training and 150 individuals from diverse professions and ethnicities for evaluation. Extensive experiments demonstrate that Proteus-ID outperforms prior methods in identity preservation, text alignment, and motion quality, establishing a new benchmark for video identity customization.

Video

Overview of Proteus-ID

framework

Built on a pre-trained DiT, Proteus-ID integrates three key components: Multimodal Identity Fusion (MIF), Time-Aware Identity Injection (TAII), and Adaptive Motion Learning (AML). Given a reference image and user prompt, MIF uses a Q-Former to integrate identity text embeddings with visual features prior to denoising. TAII incorporates timestep embeddings to adaptively modulate identity conditioning during denoising. AML enhances motion realism by introducing a self-supervised motion signal to reweight the training loss—without requiring additional inputs at inference.

Comparison

Visual Results

BibTeX

@article{Proteus-ID,
  title={Proteus-ID: ID-Consistent and Motion-Coherent Video Customization},
  author={Guiyu Zhang, Chen Shi, Zijian Jiang, Xunzhi Xiang, Jingjing Qian, Shaoshuai Shi, Li Jiang},
  year={2025}
}