Stereo World Model: Camera-Guided Stereo Video Generation

Abstract

We present StereoWorld, a camera-conditioned stereo world model that jointly learns appearance and binocular geometry for end-to-end stereo video generation.Unlike monocular RGB or RGBD approaches, StereoWorld operates exclusively within the RGB modality, while simultaneously grounding geometry directly from disparity. To efficiently achieve consistent stereo generation, our approach introduces two key designs: (1) a unified camera-frame RoPE that augments latent tokens with camera-aware rotary positional encoding, enabling relative, view- and time-consistent conditioning while preserving pretrained video priors via a stable attention initialization; and (2) a stereo-aware attention decomposition that factors full 4D attention into 3D intra-view attention plus horizontal row attention, leveraging the epipolar prior to capture disparity-aligned correspondences with substantially lower compute. Across benchmarks, StereoWorld improves stereo consistency, disparity accuracy, and camera-motion fidelity over strong monocular-then-convert pipelines, achieving more than 3x faster generation with an additional 5% gain in viewpoint consistency. Beyond benchmarks, StereoWorld enables end-to-end binocular VR rendering without depth estimation or inpainting, enhances embodied policy learning through metric-scale depth grounding, and is compatible with long-video distillation for extended interactive stereo synthesis.

Figures (15)

• Figure 1: We introduce StereoWorld, a stereo world model capable of performing exploration based on given binocular images, generating view-consistent stereo videos with intrinsic geometric understanding. StereoWorld can be applied to downstream tasks like VR/AR visualization as well as action planning in embodied intelligence. Project: https://sunyangtian.github.io/StereoWorld-web/.
• Figure 2: World Model Comparison. StereoWorld incorporates metric-scale geometry, producing output modalities that are more compatible with pretrained models. Moreover, it can be applied end-to-end for VR visualization, ensuring better consistency of fine-grained details between the left and right views.
• Figure 3: Illustration of StereoWorld. Given a pair of stereo images and a conditional camera trajectory, StereoWorld first encodes conditional and noisy video latents from different viewpoints and timesteps using a unified camera–frame RoPE representation. It then performs denoising through a DiT equipped with stereo attention, ultimately producing the final stereo video.
• Figure 4: Stereo video generation comparison with SOTA methods augmented by post-hoc stereo conversion. Our method directly generates stereo video in an end-to-end manner, enabling better preservation of inter-view detail consistency and tonal coherence.
• Figure 5: Visualization of camera trajectory comparison from methods with different camera conditioning types.