[Paper] CineScene: Implicit 3D as Effective Scene Representation for Cinematic Video Generation

Source: arXiv - 2602.06959v1

Overview

Cinematic video production often demands precise control over camera moves and subject placement, but building physical sets is expensive and time‑consuming. The paper CineScene proposes a new task—cinematic video generation with decoupled scene context—where a static 3‑D environment is captured in a few images and a model then creates high‑quality videos of a moving subject that follow any user‑defined camera trajectory while keeping the background perfectly consistent.

Key Contributions

• Implicit 3‑D‑aware scene representation: Introduces a novel conditioning pipeline that injects spatial priors from multi‑view scene images into a pretrained text‑to‑video diffusion model.
• VGGT encoder: A Vision‑Guided Graph Transformer that converts raw scene photographs into compact 3‑D‑aware feature maps, enabling the generator to “understand” geometry without explicit meshes.
• Random‑shuffling augmentation: During training, scene images are randomly reordered, forcing the model to rely on geometry rather than image order and dramatically improving robustness to varying input sets.
• Synthetic scene‑decoupled dataset: Built with Unreal Engine 5, the dataset contains paired videos with/without dynamic subjects, panoramic background renders, and ground‑truth camera trajectories—addressing the scarcity of real‑world data for this task.
• State‑of‑the‑art results: Demonstrates superior scene consistency, realistic subject motion, and faithful camera control compared with prior text‑to‑video and neural‑rendering baselines.

Methodology

1. Scene Encoding – Multiple photographs of a static environment are fed into the VGGT encoder. VGGT extracts per‑pixel visual descriptors and aggregates them into a global 3‑D‑aware latent that captures depth, layout, and texture.
2. Context Injection – The latent is concatenated with the textual prompt and fed as additional conditioning to a pretrained text‑to‑video diffusion model (e.g., Stable Diffusion Video). This “implicit” injection means the diffusion model never sees explicit geometry; it simply receives enriched feature maps that bias generation toward the captured scene.
3. Camera Trajectory Specification – Users provide a sequence of camera poses (e.g., a spline of 6‑DoF transforms). The diffusion model is guided frame‑by‑frame to render the video from those viewpoints, using the scene latent to keep background pixels coherent across frames.
4. Training Tricks –
   • Random‑shuffling of input images forces the encoder to learn order‑invariant geometry.
   • Scene‑decoupled supervision: the loss is computed on videos without the dynamic subject, ensuring the model learns to reproduce the static background faithfully before learning to blend in moving actors.

Results & Findings

• Large camera motions (e.g., 180° pans, dolly‑in/out) are handled without background tearing or flickering.
• Generalization: The model trained on synthetic UE5 scenes successfully transfers to real‑world photo sets (e.g., indoor office, outdoor courtyard) with only minor fine‑tuning.
• Ablation shows that removing VGGT or the random‑shuffling augmentation degrades LPIPS by >30 %, confirming their importance.

Practical Implications

• Rapid prototyping for filmmakers – Directors can storyboard a scene by uploading a few reference photos, specifying a camera path, and instantly generating a rough cinematic cut with actors placed via text prompts.
• Game and VR content creation – Developers can reuse existing environment assets to generate cut‑scenes or promotional videos without hand‑crafting animations.
• Advertising & Marketing – Brands can produce location‑specific video ads on the fly (e.g., “show our product in a Paris café”) without costly location shoots.
• Integration with existing pipelines – Because CineScene builds on off‑the‑shelf diffusion models, it can be plugged into current AI‑video generation APIs with minimal engineering effort.

Limitations & Future Work

• Synthetic‑data bias – Although the UE5 dataset is diverse, real‑world lighting complexities (e.g., caustics, motion blur) sometimes cause artifacts.
• Dynamic background elements – The current formulation assumes a static scene; moving foliage or crowds are not yet handled.
• Resolution ceiling – Generated videos are limited to 512 × 512 pixels; scaling to 4K will require memory‑efficient diffusion strategies.
• Future directions suggested by the authors include incorporating explicit depth supervision, extending the framework to multi‑subject interactions, and exploring few‑shot fine‑tuning on real‑world photo‑video pairs.

Authors

• Kaiyi Huang
• Yukun Huang
• Yu Li
• Jianhong Bai
• Xintao Wang
• Zinan Lin
• Xuefei Ning
• Jiwen Yu
• Pengfei Wan
• Yu Wang
• Xihui Liu

Paper Information

• arXiv ID: 2602.06959v1
• Categories: cs.CV
• Published: February 6, 2026
• PDF: Download PDF