TDFlow: Efficient and Accurate Optical Flow Estimation using Truncated Diffusion Model

Diffusion models have recently emerged as a powerful generative technique for diverse tasks in computer vision and robotic policy learning. Their extension to optical flow estimation represents a promising direction, enabling more accurate motion estimation for robot localization. Nevertheless, the heavy computational cost arising from the large number of denoising steps in vanilla diffusion policies poses a major obstacle to practical efficiency and real-world applications. To address this challenge, we propose a novel truncated diffusion policy, named TDFlow, that leverages initial flow estimates as anchors and shortens the diffusion steps by learning the denoising process directly from an anchored Gaussian distribution to the flow output, rather than the original pure Gaussian distributions. Furthermore, we design a Conditional Feature-Space Warping Diffusion Decoder for efficient flow refinement, conditioned by the scene context and flow anchors. Compared with existing approaches, our TDFlow achieves state-of-the-art performance on three optical flow datasets: MPI Sintel, Spring, and KITTI benchmarks, and further demonstrates a superior zero-shot generalization property on KITTI. Due to our designed truncated policy, our model requires only one twentieth of the denoising steps compared to the vanilla diffusion policy, while running up to $4.2\times$ faster than other diffusion-based methods.