Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation (2412.06016v3)

Abstract: While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this is because there is no explicit supervision in terms of spatial tracking at the feature level. We propose Track4Gen, a spatially aware video generator that combines video diffusion loss with point tracking across frames, providing enhanced spatial supervision on the diffusion features. Track4Gen merges the video generation and point tracking tasks into a single network by making minimal changes to existing video generation architectures. Using Stable Video Diffusion as a backbone, Track4Gen demonstrates that it is possible to unify video generation and point tracking, which are typically handled as separate tasks. Our extensive evaluations show that Track4Gen effectively reduces appearance drift, resulting in temporally stable and visually coherent video generation. Project page: hyeonho99.github.io/track4gen

• The paper introduces a novel integration of point tracking into video diffusion models to address appearance drift and enhance spatial consistency.
• It presents a trainable refiner module that refines diffusion features, boosting temporal coherence in generated videos.
• Evaluations on the VBench dataset show significant improvements in FID, CLIP similarity, and LPIPS scores, validating the framework’s effectiveness.

An Expert Overview of Track4Gen: Teaching Video Diffusion Models to Track Points for Improved Video Generation

The paper, "Track4Gen: Teaching Video Diffusion Models to Track Points Improves Video Generation," introduces an innovative framework, Track4Gen, which enhances the spatial coherence of videos generated by diffusion-based models. This paper addresses a persistent limitation in contemporary video generation models: appearance drift, where objects in generated videos gradually degrade or change inconsistently across frames, leading to a loss of visual continuity.

Key Contributions

1. Integration of Point Tracking and Video Generation: Track4Gen innovatively integrates point tracking with video diffusion models to improve visual coherence in generated videos. By merging these traditionally separate tasks into a single network, it enhances spatial supervision on diffusion features. This integration is achieved via minimal changes to existing video generation architectures, utilizing Stable Video Diffusion (SVD) as a backbone.
2. Novel Refiner Module: The development of a trainable refiner module is pivotal to Track4Gen. This module enhances raw diffusion features by projecting them into a feature space enriched with correspondence knowledge. This advancement ensures that the internal representations within the diffusion model are more temporally consistent, which directly combats appearance drift.
3. Quantitative and Qualitative Evaluations: Through extensive evaluations on the VBench dataset, Track4Gen demonstrated significant improvements in maintaining appearance constancy. Quantitative metrics, such as subject consistency and image quality assessments, alongside user studies, underscore the effectiveness of this approach. Notably, Track4Gen achieved superior results in metrics traditionally used for assessing video generation quality.

Numerical Results and Claims

The paper reports that Track4Gen reduces FID scores significantly, indicating improvements in the quality of generated videos, which surpasses the pre-trained and finetuned models using the SVD backbone. Furthermore, the framework achieved an enhancement in CLIP similarity and a decrease in LPIPS scores, indicating improved temporal consistency. The evaluations, expressed in both subjective human studies and objective metrics, strongly support the claims of enhanced coherence and reduced appearance drift.

Implications and Future Directions

The proposed framework, Track4Gen, provides a compelling solution to a critical problem in video generation. The integration of video generation and point tracking paves the way for more stable and consistent video outputs, potentially transforming workflows in fields such as animation, film production, and virtual reality content creation. Additionally, the successful implementation of a refiner module indicates potential future exploration in refining internal model features for various applications, beyond video generation.

Future directions could include the refinement of the model to handle more complex scenarios, such as occlusions and dynamic background changes. Moreover, as cutting-edge video trackers continue to evolve, Track4Gen could integrate these advancements to leverage real-world videos with automatically annotated tracks for further training, potentially extending its utility in practical applications.

In summary, Track4Gen presents a notable advancement in video diffusion models by effectively bridging the gap between video generation and point tracking. Its contributions lay a foundation for future research aimed at refining generative models through enhanced spatial awareness mechanisms.