When Physical Preferences Meet Semantic Constraints: Physical and Semantic Direct Preference Optimization for Text-to-Video Generation
Proposed PSDPO method improves physical plausibility by 2x on VideoPhy-2 while maintaining semantic consistency on VBench.
Key Findings
Methodology
PSDPO method adjusts each preference pair's contribution by combining physical and semantic signals, addressing the conflict between them. It operates within the standard DPO framework without auxiliary models or additional loss terms.
Key Results
- On the VideoPhy-2 dataset, the PSDPO method improves physical plausibility by 2x over the baseline while maintaining strong semantic consistency on the VBench dataset.
- Experiments show PSDPO achieves a VBench score of 84.13, a 1.13 improvement with the same backbone.
- PSDPO doubles physical commonsense performance on VideoPhy-2.
Significance
This research is significant in academia and industry as it addresses the long-standing conflict between physical plausibility and semantic consistency in text-to-video generation. PSDPO offers a new way to enhance video generation quality without adding complexity.
Technical Contribution
PSDPO introduces semantic constraints into preference optimization, providing new theoretical guarantees and engineering possibilities, fundamentally differing from existing SOTA methods.
Novelty
PSDPO is the first to integrate physical and semantic signals in preference optimization, addressing the systemic issue of physical-semantic conflict, offering innovation compared to prior methods.
Limitations
- PSDPO may accumulate semantic drift in early training, although staged optimization mitigates this.
- The method may perform poorly in handling extreme semantic conflict pairs.
Future Work
Future work could explore more complex physical scenarios and broader semantic constraints to further enhance model robustness and adaptability.
AI Executive Summary
Text-to-video generation models have made significant strides in visual realism, but improving physical plausibility often compromises semantic consistency with the input text. Existing preference optimization methods typically overlook the structural asymmetry between physical and semantic signals, leading to videos that are physically plausible but semantically misaligned.
This paper introduces a novel optimization method called Physical and Semantic Direct Preference Optimization (PSDPO), which adjusts each preference pair's contribution by combining physical and semantic signals, achieving a balance between them. PSDPO operates within the standard DPO framework without auxiliary models or additional loss terms, significantly enhancing training stability and convergence speed.
Experimental results show that PSDPO improves physical plausibility by 2x on the VideoPhy-2 dataset while maintaining strong semantic consistency on the VBench dataset. Additionally, PSDPO achieves a VBench score of 84.13, a 1.13 improvement with the same backbone, demonstrating a reliable balance between physical and semantic consistency. Future research could further explore more complex physical scenarios and broader semantic constraints.
Deep Analysis
Background
The field of text-to-video generation has seen significant advancements, particularly in visual realism. However, existing models often neglect physical plausibility, leading to videos that may violate basic physical principles. Preference optimization methods, such as Direct Preference Optimization (DPO), learn physical plausibility through pairwise comparisons but typically ignore semantic consistency.
Core Problem
In text-to-video generation, there is an inherent conflict between physical plausibility and semantic consistency. Existing methods usually determine physical preference by comparing dynamics between videos without considering whether either video faithfully depicts the scene specified by the prompt, leading to a systemic tendency for physical-semantic conflict.
Innovation
The PSDPO method adjusts each preference pair's contribution by combining physical and semantic signals, addressing the conflict between them. It operates within the standard DPO framework without auxiliary models or additional loss terms, significantly enhancing training stability and convergence speed.
Methodology
- �� PSDPO combines physical and semantic signals to adjust each preference pair's contribution.
- �� A gradient-level analysis shows PSDPO bounds semantic drift from conflicting pairs to a controllable residual.
- �� A staged optimization protocol is proposed to reduce cumulative drift.
- �� The method operates entirely within the standard DPO framework without auxiliary models or additional loss terms.
Experiments
Experiments are conducted on VideoPhy-2 and VBench datasets, using standard DPO as a baseline. The effectiveness of the PSDPO method is validated by comparing physical plausibility and semantic consistency metrics. Experiments also include ablation studies to assess the impact of different components on overall performance.
Results
PSDPO improves physical plausibility by 2x on the VideoPhy-2 dataset while maintaining strong semantic consistency on the VBench dataset. Additionally, PSDPO achieves a VBench score of 84.13, a 1.13 improvement with the same backbone.
Applications
The PSDPO method can be directly applied to scenarios requiring physically plausible and semantically consistent video generation, such as autonomous driving simulation and virtual reality content generation. These applications require models to maintain high levels of semantic consistency in complex physical scenarios.
Limitations & Outlook
Although PSDPO significantly improves physical plausibility and semantic consistency, it may perform poorly in handling extreme semantic conflict pairs. Additionally, semantic drift may accumulate in early training, although staged optimization mitigates this.
Plain Language Accessible to non-experts
Imagine you're cooking in a kitchen. You have a recipe (text prompt) and need to follow steps to make a dish (video). Sometimes, you might make the dish look better (physical plausibility) but stray from the recipe (semantic consistency). PSDPO is like a smart assistant that helps you make the dish both look good and taste good without straying from the recipe. It analyzes the physical and semantic signals of each step to ensure every dish meets the recipe's requirements while looking delicious.
ELI14 Explained like you're 14
Imagine you're playing a game where you need to create an animated scene based on a prompt. The scene needs to look real (physical plausibility) and fit the game's storyline (semantic consistency). PSDPO is like a super assistant that helps you create animations that follow the rules of physics and match the storyline. This way, your animations are both cool and fun!
Glossary
Physical Preference
Refers to selecting video samples that better adhere to physical laws in video generation.
Used in PSDPO to optimize physical plausibility.
Semantic Consistency
Refers to the degree to which generated videos match the input text prompt.
Used in PSDPO to ensure videos align with text prompts.
Direct Preference Optimization
A method that learns preferences through pairwise comparisons.
PSDPO builds upon this method.
Semantic Drift
Refers to the gradual deviation of generated videos from the text prompt's semantics during optimization.
PSDPO reduces semantic drift through staged optimization.
Staged Optimization
A method that introduces different data pairs in stages for optimization.
Used to reduce semantic drift in PSDPO.
Open Questions Unanswered questions from this research
- 1 How to maintain semantic consistency in more complex physical scenarios? Current methods may perform poorly under extreme conditions, requiring more robust models.
- 2 How to further reduce semantic drift during training? Although PSDPO improves this, more effective methods are needed.
Applications
Immediate Applications
Autonomous Driving Simulation
PSDPO can be used to generate physically plausible simulation driving scenarios, helping to test the safety and reliability of autonomous driving systems.
Long-term Vision
Virtual Reality Content Generation
PSDPO can be used to generate high-quality virtual reality content, providing a more realistic user experience.
Abstract
Text-to-video (T2V) generation models have achieved strong visual realism, but improving physical plausibility can come at the cost of semantic consistency with the input text. This tension arises because physical preference is typically determined by comparing dynamics between two videos, without accounting for whether either video faithfully depicts the scene specified by the prompt, making physical-semantic conflict a systematic tendency under this supervision paradigm. We formulate this challenge as a constrained preference optimization problem and propose Physical and Semantic Direct Preference Optimization (PSDPO), which modulates each preference pair's contribution based on the agreement between its physical and semantic signals. A gradient-level analysis shows that PSDPO bounds the semantic drift from conflicting pairs to a controllable residual, and further motivates a staged optimization protocol that provably reduces cumulative drift. The resulting method operates entirely within the standard DPO framework, requiring no auxiliary models or additional loss terms. Experiments show that PSDPO improves physical plausibility by up to $2\times$ over the baseline on VideoPhy-2, while maintaining strong semantic consistency on VBench, achieving a more reliable balance than existing preference-based methods.