FramePrompt: In-context Controllable Animation with Zero Structural Changes

2025-06-17

AI Summary

This work addresses the deployment challenges in controllable character animation generation—stemming from complex architectures, explicit guidance modules, or multi-stage pipelines—by proposing a lightweight, context-driven framework. Methodologically, it unifies reference images, skeletal sequences, and video frames into a single visual token sequence and builds upon a pre-trained video diffusion Transformer. End-to-end generation is achieved via latent-space motion injection and conditional future-frame prediction, requiring no architectural modifications or auxiliary guidance networks. Contributions include: (i) the first empirical validation of strong sequence-level controllability in video diffusion Transformers under visual conditioning; (ii) significant performance gains over state-of-the-art baselines in fidelity, temporal coherence, and training efficiency; and (iii) a plug-and-play design enabling straightforward real-world deployment.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Generating controllable character animation from a reference image and motion guidance remains a challenging task due to the inherent difficulty of injecting appearance and motion cues into video diffusion models. Prior works often rely on complex architectures, explicit guider modules, or multi-stage processing pipelines, which increase structural overhead and hinder deployment. Inspired by the strong visual context modeling capacity of pre-trained video diffusion transformers, we propose FramePrompt, a minimalist yet powerful framework that treats reference images, skeleton-guided motion, and target video clips as a unified visual sequence. By reformulating animation as a conditional future prediction task, we bypass the need for guider networks and structural modifications. Experiments demonstrate that our method significantly outperforms representative baselines across various evaluation metrics while also simplifying training. Our findings highlight the effectiveness of sequence-level visual conditioning and demonstrate the potential of pre-trained models for controllable animation without architectural changes.

Problem

• Injecting appearance and motion into video models
• Avoiding complex architectures for animation
• Unifying visual cues without structural changes

Innovation

• Unified visual sequence for animation
• Conditional future prediction task
• Pre-trained models without architectural changes