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SceneScape: Text-Driven Consistent Scene Generation

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arxiv 2302.01133 v2 pith:ZJLBLLOD submitted 2023-02-02 cs.CV

Rafail Fridman , Amit Abecasis , Yoni Kasten , Tali Dekel This is my paper
classification cs.CV
keywords scenedepthgenerationscenesvideosconsistentgeneratesmethod
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We present a method for text-driven perpetual view generation -- synthesizing long-term videos of various scenes solely, given an input text prompt describing the scene and camera poses. We introduce a novel framework that generates such videos in an online fashion by combining the generative power of a pre-trained text-to-image model with the geometric priors learned by a pre-trained monocular depth prediction model. To tackle the pivotal challenge of achieving 3D consistency, i.e., synthesizing videos that depict geometrically-plausible scenes, we deploy an online test-time training to encourage the predicted depth map of the current frame to be geometrically consistent with the synthesized scene. The depth maps are used to construct a unified mesh representation of the scene, which is progressively constructed along the video generation process. In contrast to previous works, which are applicable only to limited domains, our method generates diverse scenes, such as walkthroughs in spaceships, caves, or ice castles.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 24 citations worldwide. Full citation record

  1. CGGS: Consistency-Augmented Geometric Gaussian Splatting for Ego-Centric 3D Scene Generation

    cs.GR 2026-07 conditional novelty 6.0 of 10

    CGGS generates viewpoint-consistent, text-aligned ego-centric 3D scenes via consistency-augmented multi-view diffusion, flow-guided layout initialization, and mutual-information depth-refined Gaussian optimization.

  2. Quality Assessment and Distortion-aware Saliency Prediction for AI-Generated Omnidirectional Images

    cs.CV 2025-06 conditional novelty 6.0 of 10

    The authors introduce OHF2024, a human-annotated database of AI-generated omnidirectional images, and BLIP2OIQA plus BLIP2OISal models that achieve the best reported scores on this database for multi-perspective quali...

  3. Quo Vadis, World Modeling?

    cs.CV 2026-08 conditional novelty 5.0 of 10

    An agent-centric reframing of world modeling, replacing physical state prediction with 'information transitions' organized into six proxy functions and three empowerment levels.

  4. LatticeWorld: A Multimodal Large Language Model-Empowered Framework for Interactive Complex World Generation

    cs.AI 2025-09 reject novelty 5.0 of 10

    A multimodal LLM framework generates interactive Unreal-based 3D environments from text and height maps, claiming superior layout accuracy and over 90x faster production than manual methods.

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