Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models
Summary
Stream4D enhances streaming autoregressive diffusion video models by introducing a dynamic 4D reconstruction reward and motion prior to maintain coherent motion and reduce geometric drift.
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Paper page - Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models
Source: https://huggingface.co/papers/2608.19556
Abstract
Stream4D improves autoregressive video generation by replacing static 3D critics with a dynamic 4D reconstruction reward and motion prior to preserve coherent motion and reduce geometric drift.
Streamingautoregressive diffusion modelsenable real-time, long-horizon video generation, but their training objectives optimize local frame prediction rather than the geometry and dynamics of a coherent world: long rollouts accumulate geometric drift and degrade into static or unnatural motion. Recent bidirectional approaches address this problem using rewards signals built upon3D Gaussian-Splattingreconstruction. However, a single rigid 3d reconstruction cannot model a dynamic scene, so this critic penalizes genuine object motion as reconstruction error and is maximized by freezing the video. This shortcut is especially detrimental in the AR setting, where each chunk can propagate an already-static configuration. In this work, we propose Stream4D, which replaces the static critic with afeed-forward 4D reconstruction rewardthat explicitly models scene dynamics, allowing coherent motion to receive high consistency rewards. To further guide motion magnitude and quality, we add amotion priorthat rewards naturalscene-flowmagnitude while penalizing jitter and non-rigid artifacts. Our final recipe combines these two terms with a lightweightperceptual anchor. Across various autoregressive video backbones and various generation horizons, Stream4D improves4D reconstructionquality, preserves motion more effectively, and achieves higher human-aligned preference. Project page: https://banyuanhao.github.io/Stream4D/
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