# DeCo-VAE: Learning Compact Latents for Video Reconstruction via Decoupled Representation

arXiv: https://arxiv.org/abs/2511.14530 · HTML: https://mlsanity.com/paper/2511.14530/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2025.

## Built on by (most cited first, 4 of 4)

- [Physion-Eval: Evaluating Physical Realism in Generated Video via Human Reasoning](https://mlsanity.com/paper/2603.19607/) (2026)
- [VideoRAE: Taming Video Foundation Models for Generative Modeling via Representation Autoencoders](https://mlsanity.com/paper/2607.14088/) (2026)
- [Video Generation with Predictive Latents](https://mlsanity.com/paper/2605.02134/) (2026)
- [Latent Actions from Factorized Transition Effects under Agent Ambiguity](https://mlsanity.com/paper/2606.30544/) (2026)

## Abstract (arXiv)

Existing video Variational Autoencoders (VAEs) generally overlook the similarity between frame contents, leading to redundant latent modeling. In this paper, we propose decoupled VAE (DeCo-VAE) to achieve compact latent representation. Instead of encoding RGB pixels directly, we decompose video content into distinct components via explicit decoupling: keyframe, motion and residual, and learn dedicated latent representation for each. To avoid cross-component interference, we design dedicated encoders for each decoupled component and adopt a shared 3D decoder to maintain spatiotemporal consistency during reconstruction. We further utilize a decoupled adaptation strategy that freezes partial encoders while training the others sequentially, ensuring stable training and accurate learning of both static and dynamic features. Extensive quantitative and qualitative experiments demonstrate that DeCo-VAE achieves superior video reconstruction performance.