Latent-Foresight jointly learns a latent tokenizer and a flow-based dynamics model end-to-end, consistently outperforming two-stage baselines across future scene understanding tasks
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Synopsis
The work proposes Latent-Foresight, an end-to-end framework that jointly learns a latent tokenizer and a flow-based generative dynamics model, explicitly shaping the representation to support temporal predictability, with key design choices that prevent latent collapse and align reconstruction with generative objectives; experiments report more temporally coherent latent representations and consistent gains over two-stage baselines across multiple future scene understanding tasks and prediction horizons, while eliminating separate training stages, including during high-resolution adaptation.
Figure 1: Overview of Latent-Foresight . We jointly train end-to-end a VFM tokenizer encoder ℰ ϕ \mathcal{E}_{\phi} , decoder 𝒟 ψ \mathcal{D}_{\psi} , and flow-based predictor 𝒫 θ \mathcal{P}_{\theta} , while keeping the VFM backbone frozen. The encoder compresses per-frame features into a compact predictable latent space, followed by a normalization layer. The decoder is supervised by ℒ recon \mathcal{L}_{\text{recon}} . The predictor takes context latents and a noisy interpolation of the future latent as input, trained with ℒ FM \mathcal{L}_{\text{FM}} . Stop-gradient operations on both the future latent and the noisy input prevent collapse. ℒ aux-recon \mathcal{L}_{\text{aux-recon}} supervises the predicted features against the ground truth.
arXivInterpretation
An end-to-end framework that jointly learns a latent tokenizer and a flow-based generative dynamics model, explicitly shaping the latent space to support temporal predictability. Existing approaches rely on two-stage pipelines that first compress Vision Foundation Model features with fixed dimensionality reduction (e.g., PCA) or independently trained autoencoders, then train a separate predictor on the frozen latent space; other approaches apply predictors directly on raw VFM features. This work couples representation learning with temporal prediction instead of decoupling them. Summary-level evidence: the authors report extensive experiments showing more temporally coherent latent representations and consistent outperformance of two-stage baselines across multiple future scene understanding tasks and prediction horizons; specific datasets, metrics, and numbers are not given in the abstract.
Several key design choices enable stable joint optimization, preventing latent collapse and aligning reconstruction with generative objectives. In two-stage pipelines representation learning and prediction are independent, so collapse under joint optimization does not arise; this work addresses stability issues specific to end-to-end training. Summary-level evidence: the authors describe these choices as preventing latent collapse and aligning reconstruction with generative objectives, but the abstract does not list the specific mechanisms or ablations.
Performance advantages are retained while eliminating separate training stages, including during high-resolution adaptation. Two-stage baselines require separately training a compressor and a predictor; this work reports consistent gains over baselines while removing those separate stages and covering high-resolution adaptation. Summary-level evidence: the authors state that the approach eliminates separate training stages, including during high-resolution adaptation, without giving the specific high-resolution setting or numbers.
Perspective
The work targets world-modeling settings built on Vision Foundation Model feature spaces, for scenarios that require predicting future scene evolution and supporting diverse future scene understanding tasks; its aim is to make the latent space itself structured for predictable dynamics and to avoid separate training stages even during high-resolution adaptation. The authors release implementation code and model weights, supporting reproduction and comparison under the same setting.
The abstract does not list specific datasets, evaluation metrics, prediction horizon lengths, or quantitative results, nor does it detail the mechanisms that prevent latent collapse and align reconstruction with generative objectives, so the magnitude of the advantage and its applicable conditions cannot be judged from the abstract; the specific high-resolution adaptation setting is also unspecified. These are open questions that require the paper's method and experiments to resolve.