Video Generation with Predictive Latents
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| Authors | Yian Zhao et al. |
| Year | 2026 |
| HF Upvotes | 20 |
| arXiv | 2605.02134 |
| Download | |
| HF Page | View on Hugging Face |
Abstract
Video Variational Autoencoder (VAE) enables latent video generative modeling by mapping the visual world into compact spatiotemporal latent spaces, improving training efficiency and stability. While existing video VAEs achieve commendable reconstruction quality, continued optimization of reconstruction does not necessarily translate into improved generative performance. How to enhance the diffusability of video latents remains a critical and unresolved challenge. In this work, inspired by principles of predictive world modeling, we investigate the potential of predictive learning to improve the video generative modeling. To this end, we introduce a simple and effective predictive reconstruction objective that unifies predictive learning with video reconstruction. Specifically, we randomly discard future frames and encode only partial past observations, while training the decoder to reconstruct the observed frames and predict future ones simultaneously. This design encourages the latent space to encode temporally predictive structures and build a more coherent understanding of video dynamics, thereby improving generation quality. Our model, termed Predictive Video VAE (PV-VAE), achieves superior performance on video generation, with 52% faster convergence and a 34.42 FVD improvement over the Wan2.2 VAE on UCF101. Furthermore, comprehensive analyses demonstrate that PV-VAE not only exhibits favorable scalability, with generative performance improving alongside VAE training, but also yields consistent gains in downstream video understanding, underscoring a latent space that effectively captures temporal coherence and motion priors.
Engineering Breakdown
Plain English
This paper addresses a fundamental problem in video generation: while Video VAEs can reconstruct videos well, that doesn't automatically make them good at generating new videos. The authors propose adding a predictive learning objective during latent space training—essentially training the model to predict future frames—which improves how 'diffusible' (easier to work with for diffusion models) the learned latent representations become.
Key Engineering Insight
Reconstruction quality and generative quality are decoupled in latent video models. By explicitly training the latent encoder to learn predictive representations, you get latent spaces that diffusion models can more effectively sample from—a practical insight that splits optimization into two aligned but distinct objectives rather than trying to optimize reconstruction alone.
Why It Matters for Engineers
Video generation systems in production struggle with quality and computational cost. If predictive latents reduce the diffusion sampling burden while maintaining or improving output quality, this directly lowers inference cost and improves user-facing video generation speed—critical for real-time or interactive applications. It also simplifies the training pipeline by removing the disconnect between what VAEs optimize for and what diffusion models actually need.
Research Context
Prior work focused on improving VAE reconstruction fidelity assuming better reconstruction would naturally lead to better generation. This paper identifies that assumption as flawed and borrows from world modeling literature (which uses prediction as a learning signal) to show that spatiotemporal predictive learning is a better inductive bias for generative video latents. This opens a new direction: using auxiliary prediction tasks to shape latent space geometry for downstream generative tasks.
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