Uniform Discrete Diffusion with Metric Path for Video Generation (Oct 2025)

Описание: Title: Uniform Discrete Diffusion with Metric Path for Video Generation (Oct 2025)
Link: http://arxiv.org/abs/2510.24717v1
Date: October 2025

Summary:
This paper introduces URSA (Uniform discRete diffusion with metric pAth), a novel discrete generative modeling framework designed to bridge the performance gap between discrete and continuous approaches for scalable video generation. URSA frames video generation as an iterative global refinement of discrete spatiotemporal tokens. Key innovations include a Linearized Metric Path and a Resolution-dependent Timestep Shifting mechanism, which enable efficient scaling to high-resolution images and long-duration videos with fewer inference steps. Additionally, an asynchronous temporal fine-tuning strategy unifies diverse tasks like video interpolation and image-to-video generation within a single model. Experiments demonstrate URSA's superior performance over existing discrete methods and competitive results with state-of-the-art continuous diffusion models on various benchmarks, showcasing strong zero-shot generalization.

Key Topics:
Discrete Diffusion Models
Video Generation
Image Generation
Metric Path
Timestep Shifting
Asynchronous Timestep Scheduling
Text-to-Video
Video Interpolation
Long Video Generation
Zero-shot Generalization

Chapters:
00:00 - Introducing URSA's Vision
00:01:42 - Solving the Discrete Dilemma
00:03:06 - DFM & Linear Metric Path
00:05:05 - Adaptive Timestep Shifting
00:06:26 - Asynchronous Multitask Learning
00:07:59 - URSA's Performance Metrics
00:09:08 - Image-to-Video & Scaling Study
00:10:02 - The Tokenizer Bottleneck
00:11:47 - Ablation Study & Summary
00:13:02 - Tokenizers: The Next Frontier

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