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E-Motion: Future Motion Simulation via Event Sequence Diffusion
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Adapts Stable Video Diffusion to event sequences for future event-motion forecasting, adds test-time multi-frame event prompts and RL-based (PPO) motion alignment rewarded by FVD and SSIM.
Problem
Forecasting future object motion from event camera streams with high temporal resolution, where video diffusion models are pretrained on RGB but not event data and sampled futures can be unstable or physically implausible.
Approach
Fine-tune pretrained Stable Video Diffusion with event voxel prompts as conditioning, inject multi-frame high-temporal-resolution event prompts into the reverse process at test time, and align the generator with PPO using FVD+SSIM rewards.
Key results
- Ours reaches FVD 1055.25, MSE 0.0170, SSIM 0.7998 on event future prediction vs. 1218.03 FVD for the best baseline TAU (event modality).
- Test-time event prompt plus RL alignment (ablation D) improves FVD from 1378.92 to 1055.25 over the no-prompt, no-alignment baseline.
- Downstream tracking R_upper ratio of 0.891 for the proposed method vs. 0.669 for TAU, with GT event at 1.000.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use E-Motion instead of TAU for event motion forecasting | Event motion forecasting · Event motion forecasting | FVD: 1218.03 -> 1055.25 | Helped |
| use E-Motion instead of SimVP for event motion forecasting | Event motion forecasting · Event motion forecasting | MSE: 0.0210 -> 0.0170 | Helped |
| use E-Motion instead of TAU for event motion forecasting | Event motion forecasting · Event motion forecasting | aIoU: 0.532 -> 0.522 | Hurt |
| use E-Motion instead of TAU for object tracking | Object tracking · Object tracking | R_upper: 0.669 -> 0.891 | Helped |
| add high-temporal-resolution event prompt (ablation) | E-Motion · Event motion forecasting | FVD: 1378.92 -> 1227.56 | Helped |
| add reinforcement-learning motion alignment (ablation) | E-Motion · Event motion forecasting | FVD: 1378.92 -> 1119.71 | Helped |
| fine-tune spatial and temporal attention instead of temporal attention only (ablation) | E-Motion pretraining · Event motion forecasting | FVD: 1972.91 -> 1378.92 | Helped |
| use 1 training prompt instead of randomly selecting 1 to 3 (ablation) | E-Motion pretraining · Event motion forecasting | FVD: 1378.92 -> 1406.24 | Hurt |
| use event-fine-tuned CLIP instead of RGB-pretrained CLIP (ablation) | E-Motion pretraining · Event motion forecasting | FVD: 1378.92 -> 1646.78 | Hurt |
| use 4 test-time prompts instead of 1 (ablation) | E-Motion · Event motion forecasting | FVD: 1170.48 -> 1055.25 | Helped |
| use FVD and SSIM instead of a mixture of reward metrics (ablation) | E-Motion motion alignment · Event motion forecasting | FVD: 1562.66 -> 1055.25 | Helped |
Surprising findings
- Fine-tuning SVD's CLIP-conditioned U-Net with event-trained CLIP features underperforms RGB-CLIP features even after extensive fine-tuning.
Limitations
- Event data lacks texture, limiting semantic detail; complex backgrounds degrade predictions.
- Heavily overlapped objects yield less accurate motion predictions.
- Large model (1521M params, 693.92 GFLOPs) is computationally expensive for inference.
Experimental setup
Datasets: VisEvent EventVOT BS-ERGB
Benchmarks: VisEvent EventVOT BS-ERGB
Metrics: FVD MSE SSIM LPIPS mIoU aIoU AUC OP50 OP75 PR
Baselines: PhydNet SimVP TAU PredRNNv2 SVD PredRNN
Builds on: Stable Video Diffusion PPO EventSAM OSTrack
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