Archon: A Unified Multimodal Model for Holistic Digital Human Generation

2026-05-28

AI Summary

Existing methods for digital human generation lack the capability to unify multimodal information—including text, audio, motion, and visual cues—within a single modeling framework. This work proposes a human-centric, unified multimodal autoregressive architecture that jointly models seven synchronized modalities. The approach leverages modality-specific tokenizers, a semantic video reparameterization strategy that reduces video token count by 4× while preserving dynamic details, and an “intra-modality reasoning” mechanism that decomposes cross-modal tasks into stepwise reasoning chains. Integrated with multi-task pretraining and a semantics-driven video diffusion decoder, the proposed method achieves state-of-the-art or competitive performance across diverse digital human generation tasks, significantly enhancing both output fidelity and controllability.

Abstract

Digital humans are fundamental to immersive interaction, yet creating a unified model for holistic modalities, including text, audio, motion, and visual content, remains an open challenge. In this paper, we present Archon, a fully pretrained, human-centric unified multimodal model for holistic avatar generation. Archon unifies seven modalities with modality-specific tokenizers, and a native autoregressive unified multimodal model pretrained on synchronized modalities and 72 diverse tasks to model holistic joint distributions. To address the token explosion challenge in high-fidelity talking videos, we introduce a memory-efficient semantic video reparameterization, achieving 4x token reduction while preserving fine-grained dynamics, coupled with a semantic-driven video diffusion decoder. We further propose a "Thinking in Modality" that decomposes ambiguous cross-modal tasks into stepwise thinking in an alternative chain of modality, progressively enhancing fidelity and controllability. Extensive experiments demonstrate that Archon achieves superior or comparable performance across diverse digital human generation tasks, validating the effectiveness of our unified framework. Project page: https://zju3dv.github.io/archon/.

Problem

• digital human generation
• unified multimodal model
• holistic modalities
• avatar generation
• multimodal integration

Innovation

• unified multimodal model
• semantic video reparameterization
• Thinking in Modality
• autoregressive generation
• digital human avatar