Hierarchical Fine-grained Preference Optimization for Physically Plausible Video Generation

TL;DR

This paper introduces PhysHPO, a hierarchical framework for optimizing physically plausible video generation by aligning content, motion, and semantics across multiple levels, and employs an automated data selection pipeline to enhance realism.

Contribution

It presents the first approach to fine-grained preference alignment and automated data selection for physically plausible video generation.

Findings

Significant improvement in physical plausibility of generated videos.

Enhanced overall video quality and realism.

Effective hierarchical optimization across multiple granularities.

Abstract

Recent advancements in video generation have enabled the creation of high-quality, visually compelling videos. However, generating videos that adhere to the laws of physics remains a critical challenge for applications requiring realism and accuracy. In this work, we propose PhysHPO, a novel framework for Hierarchical Cross-Modal Direct Preference Optimization, to tackle this challenge by enabling fine-grained preference alignment for physically plausible video generation. PhysHPO optimizes video alignment across four hierarchical granularities: a) Instance Level, aligning the overall video content with the input prompt; b) State Level, ensuring temporal consistency using boundary frames as anchors; c) Motion Level, modeling motion trajectories for realistic dynamics; and d) Semantic Level, maintaining logical consistency between narrative and visuals. Recognizing that real-world videos are the best reflections of physical phenomena, we further introduce an automated data selection pipeline to efficiently identify and utilize "good data" from existing large-scale text-video datasets, thereby eliminating the need for costly and time-intensive dataset construction. Extensive experiments on both physics-focused and general capability benchmarks demonstrate that PhysHPO significantly improves physical plausibility and overall video generation quality of advanced models. To the best of our knowledge, this is the first work to explore fine-grained preference alignment and data selection for video generation, paving the way for more realistic and human-preferred video generation paradigms.