E-Motion: Future Motion Simulation via Event Sequence Diffusion

TL;DR

This work proposes to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework to revolutionize motion flow prediction in computer vision applications such as autonomous vehicle guidance, robotic navigation, and interactive media.

Abstract

Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capture changes in the scene with exceptional temporal granularity, may potentially offer a unique opportunity to predict future motion with a level of detail and precision previously unachievable. Inspired by that, we propose to integrate the strong learning capacity of the video diffusion model with the rich motion information of an event camera as a motion simulation framework. Specifically, we initially employ pre-trained stable video diffusion models to adapt the event sequence dataset. This process facilitates the transfer of extensive knowledge from RGB videos to an event-centric domain. Moreover, we introduce an alignment mechanism that utilizes reinforcement learning techniques to enhance the reverse generation trajectory of the diffusion model, ensuring improved performance and accuracy. Through extensive testing and validation, we demonstrate the effectiveness of our method in various complex scenarios, showcasing its potential to revolutionize motion flow prediction in computer vision applications such as autonomous vehicle guidance, robotic navigation, and interactive media. Our findings suggest a promising direction for future research in enhancing the interpretative power and predictive accuracy of computer vision systems.

Figures (14)

• Figure 1: Illustration that the exceptional temporal resolution afforded by event cameras, alongside their distinctive event-driven sensing paradigm, presents a significant opportunity for advancing the precision in predicting future motion trajectories.
• Figure 2: Inference workflow of the proposed method, where the left upper one indicates the random Gaussian noise, left lower one represents the prompted event sequence. We perform $\tau$ steps forward diffusion processing on the event prompt and substitute a portion of the diffusion input noise, followed by $T-\tau$ Steps of conventional denoising.
• Figure 3: Qualitative comparison between SOTA methods. The first row of each sequence represents the ground truth of the event sequence. The second and third rows respectively depict the results of future event estimation by SimVP gao2022simvp and TAU Tan_2023_CVPR. The final row represents the results obtained by our method. The complete sequence is shown in Fig.\ref{['fig:compare']}.
• Figure 4: More visualization of our method’s prediction in various scenarios. The results of the complete sequence along with other methods are presented in Fig. \ref{['fig:uzh']} and Fig. \ref{['fig:various']}.
• Figure 5: Visualization results on downstream tasks, where we show the tasks of tracking, segmentation, and flow estimation. (a) denotes the ceiling performance of settings (b) and (c).