[Paper] WorldReel: 4D Video Generation with Consistent Geometry and Motion Modeling

Source: arXiv - 2512.07821v1

Overview

WorldReel is a new 4‑dimensional (4D) video generation framework that produces not only photorealistic RGB frames but also a coherent underlying scene representation—including point clouds, camera trajectories, and dense motion fields. By training on a mix of synthetic data (with perfect 3D/4D supervision) and real video footage (for visual richness), the model can generate videos that stay geometrically and temporally consistent even under large camera moves and non‑rigid object motion.

Key Contributions

• Joint RGB‑plus‑4D output: Simultaneously generates video frames and an explicit 4D scene description (pointmap, camera path, dense flow).
• Spatio‑temporal consistency: Enforces a single, persistent scene across all viewpoints and time steps, eliminating the “wiggle” and “ghosting” artifacts common in existing video generators.
• Hybrid training pipeline: Combines synthetic datasets with exact geometry/motion labels and real‑world videos for diversity, achieving strong generalization to in‑the‑wild content.
• State‑of‑the‑art metrics: Sets new benchmarks on geometric consistency, motion coherence, and view‑time artifact reduction for dynamic‑scene video synthesis.
• Open‑ended representation: The generated 4D assets can be re‑rendered from novel viewpoints, edited, or used for downstream tasks such as simulation or AR/VR content creation.

Methodology

1. 4D Scene Backbone – A neural encoder‑decoder predicts a pointmap (a dense set of 3D points with color/feature attributes) for each time step, together with a camera trajectory (extrinsics per frame) and a dense optical‑flow field that ties successive pointmaps.
2. Consistency Losses – The model is penalized for mismatches between the rendered view of the pointmap and the generated RGB frame, as well as for inconsistencies in the flow‑warped geometry across time. This forces the network to keep a single underlying world that explains all frames.
3. Synthetic Supervision – On rendered scenes where ground‑truth geometry, motion, and camera parameters are known, the network receives direct supervision for all 4D components.
4. Real‑World Fine‑Tuning – A second training stage uses unlabelled video clips; only the RGB reconstruction loss is applied, while the 4D consistency terms continue to regularize the model, injecting realism without sacrificing geometry.
5. Rendering Engine – At inference, the pointmap is rasterized using a differentiable splatting renderer to produce the final frame, guaranteeing that the visual output is always grounded in the predicted 3D structure.

Results & Findings

• Quantitative gains: WorldReel improves geometric consistency scores by ~30 % and reduces view‑time flicker metrics by ~45 % compared to leading video GANs and diffusion models.
• Qualitative robustness: Test videos featuring fast pans, rotating objects, and cloth deformation show stable shapes and textures across frames, whereas baselines exhibit noticeable jitter or disappearing geometry.
• Generalization: When evaluated on wild internet videos (e.g., handheld smartphone footage), the model retains plausible 3D structure despite never having seen those exact scenes during training.
• Ablation studies: Removing synthetic supervision drops geometric fidelity dramatically, confirming the importance of precise 4D labels; omitting the flow consistency term leads to temporal artifacts.

Practical Implications

• Content creation pipelines – Filmmakers and game developers can generate background plates or dynamic assets that can be re‑projected from any camera angle, cutting down on costly 3D modelling.
• AR/VR experiences – Real‑time generation of consistent 4D worlds enables immersive scenarios where virtual objects interact naturally with generated environments.
• Simulation & robotics – The explicit pointmap and motion fields provide a ready‑to‑use world model for training perception or planning algorithms, bridging the gap between synthetic simulators and real video data.
• Video editing tools – Because the underlying geometry is available, developers can build “smart” rotoscoping, object removal, or style‑transfer tools that respect depth and motion, leading to higher‑quality post‑production effects.

Limitations & Future Work

• Resolution ceiling – Current experiments are limited to 256 × 256 frames; scaling to 4K video will require more efficient rendering and memory‑friendly point representations.
• Complex lighting – The model assumes relatively simple illumination; handling high‑dynamic‑range lighting, shadows, and reflections remains an open challenge.
• Long‑term temporal coherence – While short clips (≤ 5 s) stay consistent, drift can appear in longer sequences, suggesting the need for hierarchical or memory‑augmented architectures.
• Broader scene diversity – Synthetic training data covers a limited set of object categories; expanding the synthetic library to include more varied materials and dynamics could further improve real‑world generalization.

WorldReel marks a significant step toward video generators that think in 4D, opening up new possibilities for developers who need reliable, editable, and physically plausible visual content.

Authors

• Shaoheng Fang
• Hanwen Jiang
• Yunpeng Bai
• Niloy J. Mitra
• Qixing Huang

Paper Information

• arXiv ID: 2512.07821v1
• Categories: cs.CV, cs.AI
• Published: December 8, 2025
• PDF: Download PDF