VideoWeaver: Multimodal Multi-View Video-to-Video Transfer for Embodied Agents

TL;DR

VideoWeaver introduces a multimodal multi-view video-to-video translation framework that ensures view consistency and scalability across multiple synchronized cameras, advancing embodied AI demonstration resimulation.

Contribution

It is the first to extend V2V translation to multi-view settings using a shared 4D latent space and autoregressive synthesis for multiple viewpoints.

Findings

Achieves state-of-the-art results on single-view benchmarks.

Demonstrates physically and stylistically consistent multi-view translations.

Enables scalable multi-camera translation with dynamic viewpoints.

Abstract

Recent progress in video-to-video (V2V) translation has enabled realistic resimulation of embodied AI demonstrations, a capability that allows pretrained robot policies to be transferable to new environments without additional data collection. However, prior works can only operate on a single view at a time, while embodied AI tasks are commonly captured from multiple synchronized cameras to support policy learning. Naively applying single-view models independently to each camera leads to inconsistent appearance across views, and standard transformer architectures do not scale to multi-view settings due to the quadratic cost of cross-view attention. We present VideoWeaver, the first multimodal multi-view V2V translation framework. VideoWeaver is initially trained as a single-view flow-based V2V model. To achieve an extension to the multi-view regime, we propose to ground all views in a shared 4D latent space derived from a feed-forward spatial foundation model, namely, Pi3. This encourages view-consistent appearance even under wide baselines and dynamic camera motion. To scale beyond a fixed number of cameras, we train views at distinct diffusion timesteps, enabling the model to learn both joint and conditional view distributions. This in turn allows autoregressive synthesis of new viewpoints conditioned on existing ones. Experiments show superior or similar performance to the state-of-the-art on the single-view translation benchmarks and, for the first time, physically and stylistically consistent multi-view translations, including challenging egocentric and heterogeneous-camera setups central to world randomization for robot learning.