Title: Image as a World: Generating Interactive World from Single Image via Panoramic Video Generation
Venue: NeurIPS 2025
Accepted: yes
Keywords: visual world model

panoramic video generation
Abstract: Generating an interactive visual world from a single image is both challenging and practically valuable, as single-view inputs are easy to acquire and align well with prompt-driven applications such as gaming and virtual reality. This paper introduces a novel unified framework, Image as a World (**IaaW**), which synthesizes high-quality 360-degree videos from a single image that are both controllable and temporally continuable. Our framework consists of three stages: world initialization, which jointly synthesizes spatially complete and temporally dynamic scenes from a single view; world exploration, which supports user-specified viewpoint rotation; and world continuation, which extends the generated scene forward in time with temporal consistency. To support this pipeline, we design a visual world model based on generative diffusion models modulated with spherical 3D positional encoding and multi-view composition to represent geometry and view semantics. Additionally, a vision-language model (IaaW-VLM) is fine-tuned to produce both global and view-specific prompts, improving semantic alignment and controllability. Extensive experiments demonstrate that our method produces panoramic videos with superior visual quality, minimal distortion and seamless continuation in both qualitative and quantitative evaluations. To the best of our knowledge, this is the first work to generate a controllable, consistent, and temporally expandable 360-degree world from a single image.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel framework called Image as a World (IaaW) , which generates interactive, panoramic 360° video worlds from a single input image. The key innovation lies in its ability to not only generate high-quality panoramic videos but also allow user-controlled viewpoint navigation and temporal continuation , enabling the synthesized world to evolve indefinitely while maintaining spatial and temporal coherence. It makes a contribution to the field of generative modeling by framing panoramic video synthesis as a visual world modeling task , bridging the gap between static image generation and dynamic, interactive environment creation.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \n1) Tackles the challenging task of producing a fully interactive, temporally extensible panoramic world from just a single view.\n2) The 3D spherical RoPE is a neat extension of rotary embeddings to equirectangular video, and the multi‑view composition module offers fine‑grained control over scene synthesis.\n3) Uses diverse quality, temporal, and semantic metrics, with clear improvements over several strong baselines. Detailed ablation studies demonstrate the impact of each proposed component.\n\nWeaknesses\n1) The paper contributes primarily through engineering and system design rather than theoretical innovation. While practical frameworks are valuable, this limits its broader scientific impact.\n2) Generation of 2048×1024 videos in three stages on multiple A100 GPUs over several weeks limits practical applicability; no runtime or resource‑efficiency analysis is provided.\n3) The continuation stage conditions only on the most recent chunk, leading to drift over very long sequences, as admitted in the Limitations section.\n\nQUESTIONS:\n1) How to theoretically demonstrate the effectiveness of the method proposed in the paper?\n\n2) Can you provide inference time statistics (per frame or per segment) and discuss how the three‑stage pipeline might be optimized for real‑time or near‑real‑time applications?\n\n3) How well does IaaW handle highly complex or cluttered scenes (e.g., urban street views) compared to the mostly outdoor/natural examples shown? Could you include a failure case analysis?\n\nLIMITATIONS:\nThe authors acknowledge the key limitation that the continuation model conditions only on the most recent video chunk, causing coherence drift in very long sequences. Additionally, the requirement for multi‑GPU, multi‑week finetuning and high‑resolution datasets limits the method’s practicality. A more thorough discussion of runtime, memory footprint, and potential mitigations (e.g., chunked encoding, model distillation) would strengthen the work.", "TITLE": "Official Review", "RECOMMENDATION": 2, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-16"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a framework (IaaW) for a panaromic video generation and continuation from a single image. An image is taken as input, and a panaromic video is made from it (IaaW-IM). Then, this panaromic scene can be explored by turning around etc., which is realized using a unified spherical RoPE. Then, the panaromic scene is extended as a video from the exploration point (IaaW-CM). Experiments are conducted on a CogVideoX model finetuned on the relevant data. To help with the generation, a VLM is also trained to generate the right prompts relevant to the task.\n\nSTRENGTHS AND WEAKNESSES:\nThe paper takes on a challenging task and tries to figure out a useful framework to solve it.\n\nIt chooses to finetune a video-based diffusion model to first generate a panaromic video, thereby solving the 4D world generation task. A panaromic view can be considered 3D, and making it a video effectively renders it 4D, hence it is a world. This is an innovative approach, picking up popularity in recent times. However, since it uses a diffusion model in the backend, it may not be close to real time yet, at least not for initialization.\n\nFig.3 is quite unaligned with the corresponding text in 3.1.1. The text talks about \"parallel paths\" and \"localized 3D full attention\" suggesting that the original MMDiT is unchanged, and a gated MMDiT with the vision component being concatenated with view features. But Fig.3 indicates attention on all fields : text, vision, and vision concatenated with few features. This needs to be clarified.\n\nAlso, it is quite unclear what the masks or the view prompts are. Relevant mention is in sections 3.1.1 and 3.2.1, but they haven't been explained yet.\n\nThen, to explore the world, the paper proposes a unified spherical position embedding. This is very innovative and relevant for this task. It is to be noted that this exploration is in a static space, not dynamic : a static world is explored, then continued as a video. Nevertheless, the use of spherical embedding for panorama and extension to time for video is a nice adaptation of both techniques for this task.\n\nSection 3.2.1 and Fig. 4 show IaaW-VLM takes the world video as input, but the original task and Fig. 2 start from only a single image. It is unclear how the first video itself is given as input to IaaW-VLM before it is generated by IaaW-IM.\n\nIt is unclear why the VLM itself is so important for this task. Maybe more clarity can be achieved by knowing the use of view prompts and masks.\n\nOverall, the paper presents the problem and a simplified summary of the method very well, but the figures and methods section need improvement for clarity.\n\nThe supplementary material has very useful video examples of each stage of this task. However, the exploration part is a blank green video in each example.\n\nQUESTIONS:\n\"Unlike traditional world models that focus on action-conditioned prediction, our approach synthesizes immersive scenes that respond to user-specified actions, enabling both free-form viewpoint control and continuous scene expansion.\"\nNot sure what the difference is between \"action-conditioned prediction\" and \"synthesis of immersive scenes that respond to user-specified actions\", both seem to be the same thing.\n\nFig.3 and 3.1.1. text mismatch needs to be explained.\n\nIt is quite unclear what the masks or the view prompts are.\n\nIt is unclear why the VLM itself is so important for this task.\n\nIn the supplementary material, sample-*-2WorldExploration.mp4 and sample-*.mp4 seem to be empty flies, they just contain a blank green video.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

comments: SUMMARY:
IaaW presents a unified framework that generates controllable and temporally consistent 360° videos from a single image. It integrates world initialization, viewpoint exploration, and temporal continuation, enhanced by diffusion models, spherical 3D encoding, and a fine-tuned vision-language model for improved semantic control.

STRENGTHS AND WEAKNESSES:
### Strengths
* This work presents a groundbreaking world generation model capable of producing high-quality dynamic panoramas and supporting interactive experiences from a single image.
* The motivation behind the design of IaaW is well-founded and compelling, with its contributions validated through comparisons against FETA and the 360I2V baseline.
* The paper is well-written and easy to follow.
### Weaknesses
* The section on 3D Spherical RoPE is overly brief. While its effectiveness is demonstrated experimentally, it is recommended to include additional comparisons with other 3D positional encoding methods. Furthermore, a theoretical derivation or proof of its rotation-equivariant properties, as suggested by Equation 2, would be a valuable addition to the appendix.
* More comparative methods should be included. Although there may be no direct baselines for the entire task, fair comparisons can still be made at the "world initialization" and "world continuation" stages with recent panoramic video generation and interactive world synthesis methods (e.g., 4K4DGen, OmniDrag, WonderWorld).
* Efficiency analysis. As an interactive world generation model, the reliance on VLMs and video diffusion raises concerns about inference and interaction latency. It is recommended to include experimental results assessing the model's efficiency.

QUESTIONS:
Kindly refer to the [Weaknesses].

LIMITATIONS:
Yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 3
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces \"Image as a World\" (IaaW), a novel framework for generating interactive, 360-degree panoramic videos from a single input image. The core contribution is a three-stage pipeline designed to create a temporally and spatially coherent \"world\" that users can navigate. The process begins with World Initialization, which synthesizes an initial panoramic video from one image. This is followed by World Exploration, allowing user-controlled viewpoint rotation within the generated scene. Finally, World Continuation autoregressively extends the video sequence to create an endlessly explorable environment. To achieve this, the authors propose a diffusion-based visual world model enhanced with a novel 3D Spherical Rotary Positional Encoding (RoPE) to handle equirectangular geometry and a multi-view composition mechanism to improve scene diversity from the limited initial input. Additionally, a fine-tuned vision-language model, IaaW-VLM, generates dynamic, view-specific prompts to guide the generation process. The paper presents qualitative and quantitative results demonstrating the framework's ability to produce high-fidelity, controllable, and temporally consistent panoramic videos, outperforming baseline methods.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. This paper is very well-written, clearly organized, and easy to follow. \n\n2. The paper tackles a novel and ambitious problem: generating a complete, interactive, and infinitely extendable 360-degree world from a single image. This is a significant step beyond existing work in panoramic video generation, which often requires more comprehensive inputs (like full panoramic images or multi-view video) or lacks temporal continuity and interactive control. The problem formulation itself is a valuable contribution to the field of generative AI and world models.\n\n3. The proposed IaaW framework is well-structured and technically sound. The three-stage pipeline (Initialization, Exploration, Continuation) is a logical approach to this complex problem.  The technical innovations are well-motivated:\n\n4. The 3D Spherical RoPE is a clever adaptation of rotary embeddings for the specific geometry of panoramic videos, and the ablation studies show it effectively reduces spatial distortions. \n\n5. The authors conduct a comprehensive evaluation with both qualitative and quantitative analyses. \n\nWeaknesses:\n\n1. While the presented examples (village streets, landscapes) are impressive, the paper could benefit from demonstrating the model's performance on a wider and more diverse range of scenes. How does it handle complex indoor environments or scenes with many moving objects?\n\n2. The \"World Exploration\" stage allows for user-controlled viewpoint changes. However, during the \"World Continuation\" stage, it seems the progression of the scene is guided primarily by the IaaW-VLM's automatically generated prompts rather than explicit user commands.\n\nQUESTIONS:\n1. About the controllability: Can a user control the content of the continued world by their own language commands (e.g., \"move towards the mountains\") instead of VLM auto-generated view prompts?\n\n2. About long-term consistency: what is the maximum length of the generated video in practice?\n\nLIMITATIONS:\nN/A", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-02"}