FlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance
FlashMotion introduces a three-stage training framework combining diffusion and adversarial objectives, achieving 47× faster controllable video generation with high quality.
Key Findings
Methodology
This paper proposes FlashMotion, a three-stage training framework that first trains a trajectory adapter (SlowAdapter) on a multi-step diffusion-based video generator using diffusion loss. Next, it distills the multi-step generator into a few-step version (FastGenerator) via Score Distillation (DMD). Finally, it fine-tunes the adapter with a hybrid loss combining diffusion and adversarial objectives, employing a Diffusion Discriminator to align distributions and prevent blurring. The process includes training the adapter, distilling the generator, and joint fine-tuning with dynamic loss weighting. The framework is evaluated on a new long-sequence benchmark, FlashBench.
Key Results
- Across two adapter architectures, FlashMotion surpasses existing multi-step and distillation methods in both visual quality (FID, FVD) and trajectory accuracy (IoU). It achieves a 47× speedup, with FID as low as 14.35 on ControlNet architecture, outperforming Wan and MagicMotion. The model maintains high trajectory IoU (>70%) over long sequences.
- Ablation studies confirm that the diffusion discriminator and dynamic loss scaling are critical for reducing blurring and improving trajectory fidelity. The method demonstrates robustness across various scene complexities and object counts.
- Long video generation results show consistent trajectory adherence and high visual fidelity, validating the effectiveness of the three-stage approach for practical applications.
Significance
This work significantly advances controllable video synthesis by drastically reducing inference steps without sacrificing quality. It addresses the longstanding challenge of balancing efficiency and fidelity in long-sequence, trajectory-guided video generation. The proposed framework enables real-time applications in virtual reality, animation, and interactive media, bridging the gap between research and industry needs. The introduction of FlashBench offers a comprehensive evaluation platform for long videos, fostering further research in this domain.
Technical Contribution
The paper's core contributions include a novel three-stage training pipeline that combines diffusion and adversarial learning, a diffusion discriminator for distribution alignment, and a long-sequence benchmark. These innovations enable effective distillation of multi-step models into few-step counterparts while maintaining trajectory accuracy and visual quality. The dynamic loss scaling mechanism ensures stable training, and the framework's modular design facilitates adaptation to different architectures.
Novelty
This is the first systematic investigation into few-step, trajectory-controllable video generation. It uniquely integrates diffusion and adversarial training, introduces a diffusion discriminator for distribution matching, and develops a long-sequence benchmark. These elements collectively push the boundary of efficient, high-fidelity, controllable video synthesis, setting new standards in the field.
Limitations
- The method relies on large-scale datasets and high computational resources, limiting accessibility for some users.
- Fine-tuning still requires significant GPU time (~1K steps), hindering real-time deployment.
- Handling highly complex scenes with multiple interacting objects remains challenging, requiring further model robustness improvements.
Future Work
Future directions include reducing training costs via more efficient architectures, enhancing generalization to diverse scenes, and enabling real-time controllable video synthesis. Extending the framework to multi-modal inputs and multi-object trajectories will broaden its applicability, aiming for broader industrial adoption.
AI Executive Summary
Controllable video generation has seen rapid progress with diffusion models, yet achieving high-quality, long-sequence videos with precise trajectory control remains computationally demanding. Traditional multi-step diffusion processes, while effective, are prohibitively slow, limiting real-time applications. To address this, the authors introduce FlashMotion, a novel framework that combines a three-stage training pipeline with diffusion and adversarial objectives, drastically accelerating video synthesis.
The first stage involves training a trajectory adapter (SlowAdapter) on a multi-step diffusion generator (SlowGenerator) using diffusion loss, enabling precise trajectory guidance. In the second stage, knowledge distillation compresses the multi-step generator into a few-step version (FastGenerator), significantly reducing inference time. The third stage fine-tunes the adapter with a hybrid loss, incorporating a Diffusion Discriminator to align generated and real video distributions, preventing blurring artifacts.
This approach achieves a 47-fold speedup over previous methods, with superior visual fidelity and trajectory accuracy. To evaluate long-term performance, the authors introduce FlashBench, a comprehensive benchmark with detailed trajectory annotations for videos up to 121 frames long. Extensive experiments demonstrate that FlashMotion outperforms existing methods across multiple metrics, including FID, FVD, and IoU, on various datasets.
The significance of this work lies in its ability to enable real-time, high-quality, controllable video synthesis, opening new possibilities for virtual reality, animation, and interactive media. Despite its success, limitations include high computational costs and challenges in complex scene handling. Future work aims to improve efficiency, robustness, and multi-object control, pushing the boundaries of practical, scalable video generation.
Deep Dive
Abstract
Recent advances in trajectory-controllable video generation have achieved remarkable progress. Previous methods mainly use adapter-based architectures for precise motion control along predefined trajectories. However, all these methods rely on a multi-step denoising process, leading to substantial time redundancy and computational overhead. While existing video distillation methods successfully distill multi-step generators into few-step, directly applying these approaches to trajectory-controllable video generation results in noticeable degradation in both video quality and trajectory accuracy. To bridge this gap, we introduce FlashMotion, a novel training framework designed for few-step trajectory-controllable video generation. We first train a trajectory adapter on a multi-step video generator for precise trajectory control. Then, we distill the generator into a few-step version to accelerate video generation. Finally, we finetune the adapter using a hybrid strategy that combines diffusion and adversarial objectives, aligning it with the few-step generator to produce high-quality, trajectory-accurate videos. For evaluation, we introduce FlashBench, a benchmark for long-sequence trajectory-controllable video generation that measures both video quality and trajectory accuracy across varying numbers of foreground objects. Experiments on two adapter architectures show that FlashMotion surpasses existing video distillation methods and previous multi-step models in both visual quality and trajectory consistency.