cs.CVMar 12, 2026
SaveFlashMotion: Few-Step Controllable Video Generation with Trajectory Guidance
Organizations: Institute of Trustworthy Embodied AI, Fudan University · Shanghai Key Laboratory of Multimodal Embodied AI · Microsoft Research Asia
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.
Figures & tables
Figure 1: Illustration of the motivation and capabilities of FlashMotion . We define the SlowGenerator as the multi-step video model and the FastGenerator as its few-step distilled version. The SlowAdapter is trained with the SlowGenerator , while the FastAdapter is fine-tuned for the FastGenerator . (a) Using the SlowAdapter with SlowGenerator under few-step inference causes blurry outputs. (b) Applying the SlowAdapter to the FastGenerator degrades both quality and trajectory accuracy. (c) Finetuning the adapter with only diffusion loss still leads to blur artifacts. (d) Finetuning the adapter with existing distillation methods yields suboptimal quality and trajectory control. (e) FlashMotion achieves high-quality, accurate few-step trajectory-controllable video generation.
Figure 2: Overview of FlashMotion training pipeline. FlashMotion is trained in three stages: (1) a SlowAdapter is first trained on the SlowGenerator with a diffusion loss; (2) a FastGenerator is distilled from the SlowGenerator under the supervision of a distribution matching [ 59 ] loss; and (3) the SlowAdapter is finetuned to align with the FastGenerator using a hybrid training strategy that combines adversarial and diffusion losses.
Figure 3: (a) Architecture of FlashMotion . The trajectory adapter is finetuned upon the FastGenerator with a hybrid strategy that combines both diffusion and adversarial objectives. (b) Detailed illustration of our diffusion discriminator architecture. The discriminator adopts a DiT backbone cloned from the SlowGenerator , while several intermediate features from its DiT blocks are fed into an attention-based classifier to distinguish real videos from generated ones.
| Methods | FlashBench | MagicBench | DAVIS | Denoising Time (s) | Params (B) | ||||||
| FID( ) | FVD( ) | M/B IoU( ) | FID( ) | FVD( ) | M/B IoU( ) | FID( ) | FVD( ) | M/B IoU( ) | |||
| MultiSteps (50 Steps) | |||||||||||
| MagicMotion [ 18 ] | 20.03 | 138.83 | 68.10/73.68 | 15.17 | 107.21 | 76.61/81.45 | 50.36 | 760.95 | 53.94/72.84 | 1158.63 | 11.53 |
| Wan2.2 (ResNet) [ 40 ] | 19.03 | 139.61 | 52.19/57.76 | 21.72 | 140.41 | 62.09/67.85 | 46.44 | 703.15 | 31.22/42.74 | 333.00 | 5.02 |
| Wan2.2 (ControlNet) [ 40 ] | 16.93 | 152.04 | 65.41/71.28 | 20.05 | 157.98 | 72.80/78.46 | 43.70 | 791.80 | 52.76/71.20 | 664.53 | 10.28 |
| DragAnything [ 49 ] | 34.93 | 267.56 | 58.54/61.72 | 31.36 | 253.40 | 66.30/70.85 | 70.70 | 1166.22 | 40.13/53.60 | 589.07 | 2.21 |
Table 1: Quantitative results on FlashBench , MagicBench , and DAVIS . We report FID, FVD, and mask/box IoU (%) for both ResNet and ControlNet adapters. For each metric, the best result is highlighted in bold , and the second best is underlined . Denoising time is measured for generating 121 frames on one A100 GPU.
Figure 4: Qualitative Comparisons results. FlashMotion demonstrates superior qualitative performance, outperforming both previous multi-step trajectory-controllable methods and few-step distillation baselines.
Figure 5: Ablation studies on the FastAdapter training stage, diffusion loss, GAN loss, and the dynamic loss scaling strategy.
| Methods | FlashBench | MagicBench | DAVIS | ||||||
| FID( ) | FVD( ) | M/B IoU%( ) | FID( ) | FVD( ) | M/B IoU%( ) | FID( ) | FVD( ) | M/B IoU%( ) | |
| Adapter Type: ResNet | |||||||||
| Slow Adapter | 22.75 | 168.46 | 49.79 / 56.62 | 21.59 | 162.93 | 60.24 / 67.23 | 52.01 | 992.26 | 36.33 / 51.37 |
| w/o Diffusion Loss | 18.87 | 161.07 | 52.04 / 58.04 | 21.95 | 162.31 | 63.14 / 69.02 | 55.28 | 983.91 | 37.22 / 52.47 |
| w/o GAN Loss | 22.74 | 206.75 | 65.82 / 70.60 | 30.51 | 167.91 | 73.86 / 78.48 | 66.46 | 1015.81 | 47.13 / 62.58 |
| w/o Dynamic Scale | 26.32 | 210.93 | 65.54 / 69.77 | 21.90 | 167.00 | 73.60 / 78.15 | 73.12 | 998.85 | 47.01 / 60.12 |
Table 4: Comprehensive ablation study of FlashMotion. We analyze both adapter variants (ResNet and ControlNet) by progressively removing key components — including the FastAdapter training stage, diffusion loss, GAN loss, and the dynamic diffusion loss scaling strategy. The results show that each component plays a crucial role in preserving high video quality and precise motion alignment.
| Methods | FlashBench | MagicBench | DAVIS | ||||||
| FID( ) | FVD( ) | M/B IoU%( ) | FID( ) | FVD( ) | M/B IoU%( ) | FID( ) | FVD( ) | M/B IoU%( ) | |
| Adapter Type: ResNet | |||||||||
| VC only | 16.76 | 110.83 | 62.07 / 67.76 | 14.73 | 114.61 | 71.00 / 75.86 | 53.22 | 800.50 | 43.97 / 60.16 |
| SS+VC | 16.31 | 109.02 | 62.54 / 68.05 | 14.44 | 113.88 | 71.16 / 76.28 | 52.34 | 830.14 | 44.61 / 62.50 |
| TC+VC | 16.64 | 110.01 | 62.99 / 69.36 | 14.87 | 114.11 | 71.70 / 77.31 | 53.16 | 830.57 | 45.11 / 62.56 |
| FlashMotion | 15.81 | 108.96 | 63.96 / 70.01 | 14.16 | 109.20 | 72.34 / 77.92 | 50.58 | 786.42 | 46.74 / 64.00 |
Table 5: Ablation study on the discriminator architecture. VC denotes the Video Cross-Attention layer, SS denotes the Semantic Self-Attention layer, and TC denotes the Trajectory Cross-Attention layer. Results show that our discriminator design achieves the best overall performance across all benchmarks and metrics.
Figure 6: Additional ablation study results. Only our full method can generate videos with both high visual quality and trajectory accuracy.
Figure 7: Additional ablation study results. Only our full method can generate videos with both high visual quality and trajectory accuracy.
Figure 8: Additional ablation study results. Only our full method can generate videos with both high visual quality and trajectory accuracy.
| Method | Video Length | Video Resolution | Denoising Latency(s) | Total Params(B) | Base Model |
| LeviTor [ 41 ] | 16 | 288 512 | 80.08 | 2.21 | SVD [ 1 ] |
| DragAnything [ 49 ] | 14 | 320 576 | 589.07 | 2.21 | SVD [ 1 ] |
| SG-I2V [ 26 ] | 14 | 576 1024 | 1277.15 | 1.52 | SVD [ 1 ] |
| Tora [ 62 ] | 49 | 480 720 | 691.13 | 6.32 | CogVideoX [ 55 ] |
| MagicMotion [ 18 ] | 49 | 480 720 | 1158.63 | 11.53 | CogVideoX [ 55 ] |
| Wan+ResNet [ 40 ] | 121 | 704 1280 | 333.00 | 5.02 | Wan2.2 [ 40 ] |
Table 6: Comparison of model configurations and backbone architectures, including supported video length, spatial resolution, denoising latency, and total parameters. FlashMotion achieves the fastest denoising speed while supporting the highest resolution and longest generation length.
| Methods | Obj_Num=1 | Obj_Num=2 | Obj_Num=3 | ||||||
| FID( ) | FVD( ) | M/B IoU( ) | FID( ) | FVD( ) | M/B IoU( ) | FID( ) | FVD( ) | M/B IoU( ) | |
| MultiSteps (50 Steps) | |||||||||
| MagicMotion [ 18 ] | 53.62 | 741.91 | 67.93/83.46 | 59.37 | 697.50 | 61.05/73.47 | 52.44 | 563.38 | 66.13/72.92 |
| Wan2.2 (ResNet) [ 40 ] | 49.01 | 599.93 | 61.10/76.34 | 56.19 | 582.42 | 51.49/62.07 | 57.39 | 566.53 | 50.06/56.75 |
| Wan2.2 (ControlNet) [ 40 ] | 50.04 | 594.54 | 66.07/83.98 | 51.20 | 591.56 | 59.64/73.18 | 49.49 | 547.90 | 62.64/70.01 |
| DragAnything [ 49 ] | 76.28 | 1076.20 | 62.70/74.88 | 91.08 | 1196.46 | 53.34/63.06 | 89.26 | 1099.45 | 54.01/57.55 |
Table 7: Quantitative comparison results on FlashBench for scenes containing 1, 2, and 3 controlled objects. The detailed evaluations show that FlashMotion with a ControlNet-based adapter consistently outperforms all competing methods across all metrics, while the ResNet-based adapter also delivers superior visual quality compared to prior work.
| Methods | Obj_Num=4 | Obj_Num=5 | Obj_Num>5 | ||||||
| FID( ) | FVD( ) | M/B IoU( ) | FID( ) | FVD( ) | M/B IoU( ) | FID( ) | FVD( ) | M/B IoU( ) | |
| MultiSteps (50 Steps) | |||||||||
| MagicMotion [ 18 ] | 45.67 | 546.40 | 70.29/73.21 | 44.41 | 450.10 | 73.86/76.93 | 44.41 | 409.25 | 69.29/62.35 |
| Wan2.2 (ResNet) [ 40 ] | 61.69 | 575.65 | 50.89/53.93 | 52.04 | 476.04 | 55.56/56.98 | 41.59 | 453.60 | 44.31/41.03 |
| Wan2.2 (ControlNet) [ 40 ] | 49.25 | 503.03 | 66.15/68.65 | 43.58 | 409.57 | 70.70/70.94 | 37.11 | 406.06 | 67.27/61.05 |
| DragAnything [ 49 ] | 75.00 | 997.03 | 59.97/60.23 | 83.35 | 812.67 | 62.92/61.48 | 97.48 | 1006.25 | 56.95/49.51 |
Table 8: Quantitative comparison results on FlashBench for scenes containing 4, 5, and above 5 controlled objects. The detailed evaluations show that FlashMotion with a ControlNet-based adapter consistently outperforms all competing methods across all metrics, while the ResNet-based adapter also delivers superior visual quality compared to prior work.
Figure 9: Qualitative Comparisons results with different methods.
Figure 10: Qualitative Comparisons results with different methods.
Figure 11: Qualitative Comparisons results with different methods.
Figure 12: Qualitative Comparisons results with different methods.
Figure 13: Qualitative Comparisons results with different methods.
Figure 14: Qualitative Comparisons results with different methods.
Figure 15: Qualitative Comparisons results with different methods.
Figure 16: FlashMotion supports generating videos of different styles.
Figure 17: FlashMotion enables controllable camera movements, such as zooming in or out, by adjusting the bounding box size of the foreground object (e.g., the cup or the woman’s mask).
Figure 18: FlashMotion supports scene navigation in various environments—such as a bakery or a museum—by manipulating the bounding boxes of key objects, including the dinosaur, the mammoth, and the industrial mixer.
Figure 19: Distribution of video frame counts in FlashBench, demonstrating its support for evaluating long video generation.