Image as a World: Generating Interactive World from Single Image via Panoramic Video Generation
Authors: Dongnan Gui
Xun Guo
Wengang Zhou
Yan Lu
Venue: NeurIPS 2025 poster ()
Keywords: visual world model
panoramic video generation
Primary area: applications

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.

Image as a World: Generating Interactive World from Single Image via Panoramic Video Generation
Yan Lu² University of Science and Technology of China Microsoft Research Asia (gdn2001 @mail., zhwg @ |ustc.edu.cn (xunguo, yanlu | @microsoft.com
Dongnan Gui
Xun Guo
Wengang Zhou'
World Initialization
2 World Exploration
3 World Continuation
Front Video
Input Image
Right Video
Left Video
Front Video
Input Image
Right Video
Back Video
Figure 1: Generated world by our laaW. Each line represents a fixed-view view video that includes our proposed three stage of visual world generation: initialization, exploration, and continuation.
Abstract
Generating interactive visual world from single image is both challenging and practically valuable, as single-view inputs are easy to acquire and align well with prompt-driven applications such gaming and virtual reality. This paper introduces novel unified framework, Image as a World (IaaW), which synthesizes high-quality 360-degree videos from single image that both controllable and temporally continuable. Our framework consists of three stages: world initializa- tion, 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
Work done during internship at Microsoft Research Asia.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

time with temporal consistency. To support this pipeline, we design visual world model based on generative diffusion models modulated with spherical 3D positional encoding and multi-view composition to represent geometry and view semanties. Additionally, a vision-language model (laW--VLMM is fine-tuned to produce both global and view-specific prompts, improving semantic alignment and controlla- bility. 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 our knowledge, this is the first work to generate controllable, consistent, and temporally expandable 360-degree world from a single image.
1 Introduction
Recent advances in world models 11 21 and video generation have enabled simulation and extension of environments in rich, multimodal ways. World models have evolved handle raw visual inputs, producing videos conditioned on actions or inferred intent across domains such as robotics [41
18 autonomous driving [48] [0, and interactive gaming [4]. Concurrently, large-scale diffusion
models 17 28 43 and vision transformers (7 have redefined the frontier of video generation, achieving high fidelity and temporal coherence across diverse conditions. These advancements collectively point toward a promising new direction: building dynamic, controllable, and immersive environments directly from visual cues.
In this work, we take a step further by proposing a novel problem: generating an explodable and temporally extendable panoramic world from single image-one that not only predicts future frames, but also supports interactive viewpoint control, enabling arbitrary view rotations and continuous scene evolution. Compared to prior methods that rely on multi-view or panoramic input, our single-image setup significantly reduces the cost of data acquisition and aligns with the growing trend of prompt- based generative models. Unlike traditional world models that focus on oetion-conditioned prediction, our approach synthesizes immersive scenes that respond to user-specified actions, enabling both free-form viewpoint control and continuous scene expansion.
Generating such a visual world from single image is both practically appealing and technically challenging. requires the model to infer latent geometry, spatial layout, and temporally coherent dynamics from highly limited visual evidence an under-constrained and ill-posed task. We formulate this as new direction in panoramic video generation, where the goal is to synthesize panoramic, navigable, and temporally extensible video from minimal visual input.
Existing methods are not designed for this setting. Many prior approaches to panoramic video gener- ation adopt one-shot generation strategies without temporal continuity or interaction capability, For instance, 360DVD [38) relies on text-to-video models with limited resolution, while 4K4DGen [22] generates each frame independently without temporal coherence. Others assume richer input such as multi-view vidcos (4, 255 or full panoramic images (20 23).
To address these challenges, we structure our solution as a three-stage generative pipeline shown in Fig. 22 (1) World Initialization, which synthesizes spatially complete and temporally coher- ent panoramic video from a single image, which provides stable spatiotemporal foundation for the subsequent stages; (2) World Exploration, which enables interactive navigation by modeling viewpoint changes as actions, thereby embedding user control directly into the generation process; and (3) World Continuation, which extends the scene forward in time while maintaining temporal consistency beyond a fixed horizon. Each stage addresses a specific limitation in prior work, and they collectively enable consistent, controllable and infinitely extensible world synthesis. To support this pipeline, we design visual world model, implemented by augmenting a diffusion- video generator with 3D Spherical Rotary Positional Encoding (RoPE) and multi-view composition. These components equip the model with the ability to represent scene geometry and maintain diversity across dynamic panoramic sequences. To enhance controllability and prompt alignment, we also finetune vision-language model (laW-VLMM that generates semantically grounded, view-specific prompts conditioned on the user's perspective. Comprehensive experiments demonstrate that laaW capable of generating high-fidelity, semantically plausible panoramic videos that are both spatially coherent and temporally smooth. To our knowledge, this the first framework to achieve infinitely expandable, user-controllable panoramic world synthesis from single image.

---

laaW-VLM
Prompt,
World Initialization
laaW-IM
Pal
Pirs
World Video V1
laaW-VLM
View Prompts
World Exploration
A8,
World Video V
V/¹"
Repeat
Video Prompt
laaW-VLM
World Continuation
laaW-CM
World Video Vx-1
World Video V
Figure Pipeline of our proposed laaW method, which consists three core stages: world initialization, world exploration, and world continuation. In the world initialization stage, given single reference image. we employ our finetuned laaW-VLM to produce a holistic video-level prompt alongside multiple view-specific prompts. These. in conjunction with the input image, processed by our MaW-InitialModel (laaW-IM) to generate the initial world video Ví. World exploration stage enables user's spatial control over the generated scene, the rotation module transforms the video Vi to reflect the desired viewpoint video V/ In the final world continuation stage, the rotated video and its associated prompt are fed into the MaW-ContinualModa (laaW-CM), which produces an extended segment of the world. This process is inherently recursive, allowing the newly generated video to undergo further view rotations and extensions.
2
Related Work
2.1 World Model
21 aim to predict the future evolution of an environment in response to specific actions. Traditionally. these models operated in abstract spaces and predominantly used for planning [14] 30
World models
30 or policy learning 13 in reinforcement learning contexts. Recent advances in generative modeling have extended world models the visual domain, enabling video generation conditioned on control inputs [49]. In autonomous driving 48 10], models predict based on driver
actions, while in robotics [8] predictions conditioned control signals of robots. Genie [4) further generalizes this by learning action-conditioned dynamics from raw gameplay videos in an unsupervised manner and WonderWorld [44] focuses on static 3D scene world generation using Coussian-Splatting
representation from single image. Notably, 3D-based world generation like WonderWorld [44] does not produce equirectangular video but instead reconstructs a 3D world scene using Gaussian splatting or other representations, which may enable longer exploration paths but often suffers from artifacts inherent splatting such as blur and point cloud sparsity. contrast, our method generates temporally consistent and spatially coherent dynamic video with spherical field of view, offering a more immersive and artifact-free experience. Leveraging the interactive nature of the panoramic videos, we treat user-specified view rotations as world actions and generate the corresponding next-step visual evolution, enabling immersive, controllable, and continuable world generation.

---

2.2 Video Generation
26 have propelled video generation. with hierarchical U-Net (29] and diffusion vision transformer (DiT) [27] architectures leading the way spatiotemporal modeling. Approaches like Imagen Video [6 and Make-A-Video 32 extend these models with temporal attention, while Sora [ scales diffusion transformers for video synthesis. In the open-source landscape, Cog VideoX [43] introduces a 3D causal VAE with adaptive LayerNorm for efficient spatiotemporal modeling, and Hunyuan Video [39] employs dual-stream transformer for enhanced text-video alignment. Wan [37] addresses high-resolution generation with dynamic 3D-VAE compression. These models highlight the growing trend toward hybrid architectures and scaling strategies for balancing fidelity and efficiency.
Recent advancements in diffusion models [3 17
28
2.3 Panoramic Video Generation
Recent advances in panoramic video generation explore diverse paradigms 38 25 20
360DVD 38
the first to tackle text-to-panorame video synthesis by integrating 360-adapter into early-stage T2V pipelines. However, it is limited by low-resolution training data and underpowered base models, yielding suboptimal quality. 4K4DGen [22] proposes a training-free approach for animating 4K
panoramic images by independently rendering perspective views and spatially fusing them, while OmniDrag [33] enables interactive control via drag-based motion manipulation. DynamicScaler [20] enhances spatial scalability using offset-shifting denoiser to synthesize spherical panoramas, fol- lowed by learned upscaling stage. Several works address panoramic video generation by outpainting from nFOV inputs. VideoPanda [42] introduces multi-view attention to maintain spatiotemporal consistency, whereas [25] reframes the task as vidco-to-video generation. In driving applications, Panacea [40] leverages BEV representations for conditional panoramic synthesis. In contrast, our method directly generates high-fidelijy temporally coherent and continuable panoramic videos from single image, achieving both spatial diversity and temporal infinity without auxiliary inputs.
Method
3.1 Visual World Model
Our visual world model is built on a diffusion- based video backbone, enhanced with multi- view composition and 3D Spherical RoPE. To support different goals. we introduce two finetuned variants: IndW-InitaalModel (laaW-IM) for world initialization and Iaa W- Modt (laaW-CM for world continu- ation. While sharing the same architecture, the two models are optimized for different stages, which are scene reconstruction from single image vs. temporally coherent extension.
padding
Input Image
Noisy Frames
View Prompts
Video Prompt
*test
vvision
Zeision
Zteart
Fyrl
vvision
Marh
3.1.1 Multi-View Composition
3D Full Attention
To address the limitations of one-shot condition- ing in existing video generation models, pro- pose a multi-view composition method that sig- nificantly enhances the quality and diversity in world initialization. This mechanism takes two inputs, view masks, which binary masks that correspond to user-specified predefined views (e.g., front, left, top), and view prompts, which are prompts aligned with each masked region generated by our Ia.--VLM, providing tex- tual guidance for content generation from that specific viewpoint. As depicted in Fig. 3 our method begins with single reference image,
Gate
Feed Forward Network
Figure 3: Multi-view composition used in laaW. IM's MM-DiT blocks world initialization.

---

corresponding video prompt, and set of auxiliary view prompts, each of which is paired with spatially aligned masks. These view prompts are semantically and spatially diverse renderings the scene, intended to provide additional geometric and contextual priors.
4 which uses MM-DiT Building on recent powerful video generative models, such Cog VideoX
blocks [8] and concatenate textual (Ztex) and visual (#vision) features, we introduce multi-view conditioning mechanism for improved world initialization. Here, Stest comes from the main video prompt, and Avision encodes padded reference image with noisy frames. We add parallel attention path using view-aware features: 2 from IaaW-VLM is concatenated with 2vision and modulated by view masks for localized 3D full attention. This stream runs in parallel with the base attention and is adaptively gated fuse multi-view cues with global context. For elarity, AdaLN and scale-and-shin components are omitted from Fig.
3.1.2 3D Spherical RoPE
We propose a unified 3D Spherical Rotary Positional Encoding (RoPE) that extends traditional
rotary embeddings [35 43 to spherical video domains. By embedding positional information in both spherical space 66 45 47 and time, our method aligns with the geometric structure of equirectangular panoramic video while preserving the enation-equivarian properties of RoPE.
Let video V RR represent sequence of frames with height H, width W, feature dimension D, and temporal length T. Each spatial coordinate (æ: y) is mapped to spherical angles via:
0 0- π (2-11) Ô=
2x W -1).
where A and o denote latitude and longitude, respectively. We then construct a unified 3D positional encoding by modulating angular and temporal components in a factorized trigonometric basis:
RoPEa,,. [cos(2 cos(2 d) cos(24 2rt), sin(2 cos(2"
cos(2" 2 (2)
which compactly encodes the 3D positional across spatial angles (0, Φ), frequency d and normalized time f. 3D Spherical RoPE captures rotational symmetries on the spherical surface while enabling temporal phase alignment, resulting compact and geometry- aware encoding mechanism for panoramic video generation.
3.2
IaaW Pipeline
3.2.1
World Initialization
World Initialization serves as the entry point for visual world synthesis, which establishes the spatiotemporal foundation for subsequent user- controlled exploration and continuation. Given only single-view image, the model must gen- erate an initial panoramic video clip that is both spatially complete and semantically coherent, despite the severe ambiguity posed by missing multi-vier context.
Initialization Stage
Continuation Stage
Image
World Video Vy'
Input
Input
To enhance semantic suitability and consis- tency in video generation, introduce world context model laaW-VLM that generates both global and view-specific prompts. For each cquirectangular video V, we first employ a cap- tion model for a global prompt ! that summa- rizes the entire scene. The video is then spatially segmented into multiple views {V,) and indi- vidually captioned to yield prompts [Po}, cap- turing the localized context. From each V,, we extract a representative frame 7,,, which forms the dataset (V, P. (V,, Px), }. This corpus supports training for laa--VLM, whose functionality shown in Fig. 4 IaaW- W-WLW-LM can generate {P.}, P from single view image I, and P from video V.
laaW-VLM
laaW-VLM
Video Prompt
Video Prompt P
View Promots P.
Output
Output
Figure 4: Functionality of laaW-VLM.
5

---

which supports laaW-IM and Iaa IaaW-CM separately. By grounding generation in this multi colti-granular context, coherence.
acquires an enriched understanding of both spatial structure and temporal
With visual world model laaW-IM and above Ia.,-VIM, as shown in Fig. send entire video prompt and view prompts with corresponding masks into Iaa M-M- model. This process generates the world video as
Vi = IIMI, Pi, (M, Pe, v E views))
where P1 and P. represent initial prompt and view prompts respectively, M, represents masks.
3.2.2 World Exploration
Panoramic video enables immersive navigation by allowing users to rotate their virtual viewpoint within a spherical environment. We model this interaction as a transformation in spherical coordinates applied to the kth equirectangular video V "x* w DxT where W = 2H, and D, T denote the channel and temporal dimensions. Each pixel (a,y) E [0, W) x [0, H) corresponds to spherical
coordinates (f, Φ) following Eq. ( These angles represent latitude O | 2 2] and longitude
[- πT) User-specified pitch and yaw rotations (A0, AΦ) E R22 simulate view changes by adjusting the angles:
o'= 0' = clip(0 + 20, g.
Here, clip ensures that the elevation stays within the bounds of the spherical domain. To map back to image coordinates, we have
-0'
1 (0'+ 2π + 2 W mod W, y/ y/
--=
0' H
x' =
π 2
The complete process yields the rotated video V/ E RBXWWDXT, which is obtained by sampling the original video at Ve(2', v f) for each (x, t)
3.2.3 World Continuation
View-aware world continuation stage enables the synthesis of temporally extended and visually coherent video sequences conditioned user-defined reference view. Our approach is built upon the visual world model Iaa which operates in an autoregressive manner, progressively generating video segments while maintaining view and content consistency over time in Eq. (6)
Vi+1 CMVV
=
(6)
Specifically, following the paradigm of laaW-IM, we substitute the single view image with video V/ from previously rotated video chunk. At step k. the IaaW-VLM produces the next prompt Ps+1 based on the evolving visual context, guiding the generation of segment V towards arbitrary length. This stage establishes foundation for open-ended and infinite scene generation, where coherent and semantically meaningful world can emerge over extended temporal horizons, grounded in a user-defined viewpoint trajectory.
4 Experiments
4.1
Experimental Setup
Models In the field of video generation, there are few open-source video diffusion models available for experimentation. We use Cog VodeooX....SS--B that supports arbitrary resolution and is well suited our 2:1 aspect-ratio video setup. We use equirectangular videos to finetune Iaa M-IM where the input image is padded before being fed into the model. laaW-CN C-C is finetuned on top of laaW- I-M, using the previous video chunk as input and the next video chunk as output. Finetuning is conducted over two weeks on 4xA 100 GPUs, followed by one week of progressive finetuning. Due to the absence of released code from prior panoramic we implemented two baselines for comparison. One is 36012V, a panoramic animation baseline fine-tuned from Cog VideoX, which takes panoramic image as input to generate panoramic vidcos. Another is FETA (First Expand, Then Animate), two-stage bascline for world
[43], a text-image conditional video generator
methods 220 25 22

---

Input Image
PanoFrame w.o. SphereROPE
PanoFrame with SphereROPE
Figure 5: The results reducing distortions 3D Spherical RoPE in world initialization.
monden diuciooe Volencr with thanched roet sutrarr Arsowss and contiectone... .cor
WLOW-VLM
tanqui iKlage souare with cochlestone pests. a stone welk, matened houdes, and clettan mountees........................
surrundded many wooden cooblestone foorand.....
Figure 6: Generation results of multi-view composition for world initialization.
initialization, where we combine Diffusion360 [9 for NFo panorama expansion with 36012V for subsequent animation. We compare world initialization results with FETA, compare the world continuation results with 36012V, and compare our whole laaW pipeline with FETA+360I2V.
NooV-to-panorama
Data We consider several panoramic video datasets, including WEB360 38 and 360-1M from ODIN 36 Due to WEB360's s limited scale and low resolution (2K videos at 1024 512), excluded from our study. From 360-1M 1M, we curate high-resolution, equirectangular subset by filtering out static scenes and selecting diverse, dynamic content. Captions are generated using VL-255 (2), and low-quality samples are removed based caption quality. Augmented with an internal collection, our final dataset comprises 10KK videos at 2048 x 1024 resolution. An 8K high-quality subset is further collected for progressive finetuning.
Metrics To evaluate video generation quality, we consider both overall and per-view fidelity and consistency using metrics from VBench [19] and VideoBench (15), Subject Consistency measures temporal coherence via the average cosine similarity DINO (5) features between each frame and the first. Motion Smoothness is quantified by the mean absolute error between interpolated and dropped frames, while Aesthetic Quality predicted using the LAION aesthetic model [T. Video-Text Consistency assesses semantic alignment with the prompt, computed the average score (1-5) assigned by a vision-language model. To evaluate continuous generation results, we concatenate videos from preceding steps, rotational transitions, and subsequent generations evaluate coherence over extended sequences.
4.2 Qualitative Analysis
World Initialization Results We first demonstrate the effectiveness of our 3D spherical RoPE in Fig. 5 The figure compares panoramic frame produced by our IaaW-IM. When rendering the panoramic image from specified viewpoint, the model with spherical RoPE exhibits fewer distortions. In particular, it preserves the correct perspective geometry of structures such as the pavilion, whereas the model without that yields deformed objects with incorrect perspective relationships.
To assess the impact of multi-view composition in our initialization model laaW-IM, we visualize generation results under varying view prompts in Fig. 6 Using fixed video prompt and identical spatial masks, we observe that distinet view prompts (e.g., wooden buildings vs. flower yards) yield
semantically diverse scene expansions. This demonstrates the fine-grained controllability afforded by

---

Inout
World Intiaaicaation Results by our lad
Panotmage
World Initialization Results by FETA via Diffusion380 38012V
Figure World initialization results compared with First Expanding Then Animating(EETA).
Input Image
10 Frame of Genersted World
50 Frame Genersted World
100" Frame Genersted World
150 Frame of Genaratad World
200" Frame of Generated World
300" Frame of Generated World
400 Frema Genersted World
500" Frame of Generated World
600 Frame of Generated World
800" Frame of Generated World
1000 Frumn of Generated World
Figure 8: Long panoramic world video generated by our IaaW.
multi-view composition mechanisms and underscores their ability to guide content-specific scene synthesis during world initialization.
In Fig. 7 we compare our initialization strategy with a baseline that first performs panoramic extrapolation then animates the results in two separate stages. This decoupled spatial-temporal generation often leads to pronounced spatial artifacts and temporal discontinuities. In contrast, our method jointly models spatial structure and temporal dynamics and delivers coherent expansions that maintain global scene structure while enabling temporally smooth motion, establishing a superior world base.
We also include an example of a long panoramic world video generated by our laaW in Figure Fig. Our three-stage method successfully converts single input image into relatively long panoramic world. Specifically, our chunk-by- approximately ten rounds. The results begin to drift during super-long continuations like beyond minutes, leading to poor and vague content. Addressing this long-range coherence issue is a core problem across the field and reserved for future work.
method maintains high-quality results within the first minute
World Continuation Results We evaluate the continuation model IaaW-CM in Fig. where the initialized world is aerial view towards a lighthouse. Our model maintains directional consistency across extended sequences after rotational transformations. Specifically, our generated continuation video persistently advances toward the lighthouse while remaining both temporally stable and spatially coherent. In comparison, the baseline, which conditioned solely on the last frame, suffers from abrupt motion discontinuities and visual degradation, and fails to preserve global motion dynamics. These findings highlight the efficacy of our IaaW-CM in capturing long- cong-consistent motion trajectories.

---

World Initialization
World Exploration
World Continuation by 36012V
World Continuation by Our laaW-CM
Figure 9: World continuation results of our Iaa R-CM compared with baseline 36012V.
Overall, our laaW framework demonstrates qualitatively superior results in both initialization and continuation stages, affirming its effectiveness in generating visually coherent, controllable, and temporally consistent panoramic video worlds.
4.3 Quantitative Analysis
We present the quantitative results in Table [ where evaluate the videos in three setups: world initialization videos, world continuation videos, and entire world videos. The latter refers to concatenated video of initialization video, world exploration video, and world continuation video. We evaluate the results using two distinct methods: "All", which assesses the entire video in an equirectangular format, and "View", which calculates the average score after cutting the video into several individual views and evaluating each.
Our Iaa W-IM outperforms the baseline FETA across most metrics, demonstrating superior spatial- temporal quality. Temporal metrics averaged across views are higher than overall due to motion discontinuities introduced by splits in the equirectangular format. Aesthetic quality is lower when averaged per view, as certain angles (e.g., top, bottom) naturally lack visual appeal (e.g., sky, floor). VTC- View scores are lower than VTC-AlI because some view-specific videos inadequately capture the full prompt, reducing alignment.
In continuation model comparisons, our IaaW-IM outperforms the baseline models 36012V and 4K4DGen (22) across most metries, indicating stronger spatial and temporal modeling. 4K4DGen is an image animation baseline method capable of processing high-resolution images to 4K. As this method does not involve text, the metric for view-text consistency is omitted here. Our IaaW method surpasses the 4K4DGen baseline by offering view change, world continuation, and language of features
control, in addition producing superior video generation effects. This comprehensive highlights the advanced capabilities of our IaaW framework. Temporal and spatial metrics trends mirror those initialization models, but SC-View lower than SC-All due to reduced uncertainty when the full panoramic image is available. Temporal metrics surpass those of initialization models as full panoramic input offers richer context than single-view inputs. Slightly lower spatial scores stem from decreased diversity and aesthetic richness when multi-view information is provided.
For whole-process comparison, our Iaa C surpasses the baseline FETA+36012V across all metrics, demonstrating enhanced temporal consistency and spatial quality. Specifically, the overall results are relatively lower than those of the continuation stage. This difference arises because the combination of the initialization and continuation stages makes achieving temporal smoothness more overall result evaluates the concatenated
challenging. Since each stage has own specifications, videos to achieve a balanced performance metric. By effectively integrating initialization and continuation models, our pipeline generates visually consistent results, whereas the baseline exhibits fragmentation between two stages, leading inferior performance.

---

Model
SC-View
SC-AII
View
MS-AII
AQ-View
AQ-AII
VTC-View
VTC-AII
FETA
89.4
86.8
98.1
98.3
49.9
56.7
3.19
3.93
laaW-IM
91.8
88.2
99.0
98.9
55.9
59.8
3.72
4.00
36012V
92.5
94.8
3.25
3.89
4K4DGen
94.1
95.1
99.2
98.8
46.0
53.4
MMW-CC
95.8
97.2
99.3
99.2
49.7
55.7
3.26
3,90
6ETA436012V
81.0
88.7
98.8
98.7
50.1
55.9
3.39
3.93
IaaWIIM+CM
91.0
90.3
99.1
99.1
50.5
57.5
3,50
3.94
Table 1: Analysis of video generation results of our method and several baselines. SC, MS, AQ and VTC represent subject consistency, motion smoothness, aesthetic quality, and video-text consistency these metries, higher scores are better. Postfix "View" means the num-
respectively, and for bers are calculated across different views and "All" means the numbers are calculated whole. 4K4DGen can generate 4096 x x048 resolution video, while for comparison, we include the results using the 2048 1024 video resolution here.
Model
SC-View
SC-AII
MS- VieW
MS-AI
AQ-View
AQ-AII
VTC-View
VTC-AII
TaaW-IM
91.8
88.2
99.0
98.9
55.9
59.8
3.72
4,00
IaaW-IM w.o. 3D SphereRoPF:
86.3
83.7
98.1
97.9
48.8
56.8
3.17
3.97
IaaW-IM w.o. MultiVie
91.2
86.6
99.0
98.9
49.5
59.9
3,24
3.95
Table 2: Ablation study on our world initialization model IaaW-In W.I.
4.4 Ablation Study
We conduct an ablation study world initialization components in Table 2 Removing the 3D Spherical RoPE consistently degrades performance both in spatial and temporal metries, especially for riew-based metrics. This degradation is primarily due to spatial distortions, resulting in unsmooth motion and scene deformation. Excluding the Multi-Vien Composition module reduces VTC-View and AQ-View it limits the model's ability to capture view-specific textual cues and leads loss of visual quality in separate views. Temporal metrics remain relatively stable, since this module mainly enhances diversity rather than motion smoothness. The slight drop in VTC-AlI suggests the model still generates prompt-aligned content overall, as neither component directly influences overall textual understanding in video generation.
Limitations and Social Impact
laaW excels at generating panoramic world from a single image but struggles with maintaining temporal consistency over very long durations. Specifically, IaaW-CM conditions on the most recent video chunk rather than the full video history, which can lead loss of coherence during super long-term video continuations. This long-term consistency presents a key challenge not only for laaW but also for the broader field of video generation, which we leave future work.
From a societal perspective, laaW empowers content creation across VR/AR and gaming, potentially opening new avenues for immersive interactive experiences. However, it also introduces risks related to misinformation and visual deception, which may undermine trust in visual media. Implementing robust safeguards is essential to mitigate potential misuse and ensure responsible use.
Conclusion
We introduce Image as a World (laaW), a novel framework for generating expandable, user- controllable panoramic world from single image, which comprises three critical components: world initialization, world exploration, and world continuation. We design visual world models equipped with 3D spherical RoPE and multi-view composition, and two variants of which, laaW-IM and W-CM, tackle world initialization and continuation, respectively. Extensive experiments validate the effectiveness our approach, demonstrating high fidelity, controllability, and scalability across diverse scenarios. Our work opens new potential for one-shot visual world generation in applications such as gaming and virtual reality, setting the stage for future research generating interactive visual worlds.
10

---

References
[1] LAION AL Laion-acstheties.
[2] Shuai Bai, Keqin Chen, Xuejing Liu, Jialin Wang. Wenbin Ge, Sibo Song, Kai Dang. Peng Wang. Shijie Wang. Jun Tang, Humen Zhong, Yuanzhi Zhu, Mingkun Yang. Zhaohai Li, Jianqiang Wan, Pengfei Wang. Wei Ding. Zheren Fu, Yiheng Xu, Jiabo Ye, Xi Zhang. Tianbao Xie, Zesen Cheng. Hang Zhang, Zhibo Yang, Haiyang Xu, and Junyang Lin. Qwen2.5-vl technical report. arXiv preprint arXiv:2502. 13323, 2025.
[3] Tim Brooks, Bill Peebles, Connor Holmes, Will DePue, Yufei Guo, Li Jing, David Schnurr, Joe Taylor, Troy Luhman, Eric Luhman, Clarence Ng. Ricky Wang, and Aditya Ramesh. Video generation models as world simulators. 2024.
[4] Jake Bruce, Michael D Dennis, Ashley Edwards, Jack Parker-Holder, Yuge Shi, Edward Hughes, Matthew Lai, Aditi Mavalankar, Richie Steigerwald, Chris Apps, al. Genie: Generative interactive environments. In Forty-first International Conference on Machine Learning, 2024.
[5] Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in sule--sue vision transformers. In Proceedings of the IEEE/CVF international conference on computer vision, pages 9650-9660 2021.
[6] Zhaoxi Chen, Guangcong Wang. and Ziwei Liu. Text2light: Zero-shot text-driven hdr panorama generation. ACM Transactions on Graphics (TOG), 41(6):1-16, 2022.
[7] Prafulla Dhariwal and Alexander Nichol. Diffusion models beat gans on image synthesis. Advances in neural information processing systems, 34:8780-8794, 2021.
[8] Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Bocsel, al. Scaling rectified flow transformers for high-resolution image synthesis. In Forty-first international conference on machine learning, 2024.
[9] Mengyang Feng. Jinlin Liu, Miaomiao Cui, and Xuansong Xie. Diffusion360: Seamless 360 degree panoramic image generation based on diffusion models. arXiv preprint arXi::2311.13141 2023.
[10] Shenyuan Gao, Jiazhi Yang, Li Chen, Kashyap Chitta, Yihang Qiu, Andreas Geiger, Jun Zhang, and Hongyang Li. Vista: A generalizable driving world model with high fidelity and versatile controllability. arXiv preprint arXiv:2405. 173988 2024.
[11] David Ha and Jürgen Schmidhuber. Recurrent world models facilitate policy evolution. Advances in neural information procesing systems, 31, 2018.
[12] Yoav HaCohen, Nisan Chiprut, Benny Brazowski, Daniel Shalem, Dudu Moshe, Eitan Richardson, Eran Levin, Guy Shiran, Nir Zabari, Ori Gordon, et al. Ltx-video: Realtime vidco latent diffusion. arXiv preprint 1501::2501.00100 2024.
[13] Danijar Hafner, Timothy Lillicrap. Jimmy Ba, and Mohammad Norouzi. Dream to control: Learning behaviors by latent imagination. arXiv preprini arXiv:1912.0166 2019.
[14] Danijar Hafner, Timothy Lillicrap, Mohammad Norouzi, and Jimmy Ba. Mastering atari with discrete world models. arXiv preprint 2010002000 2020.
[15] Hui Han, Siyuan Li, Jiaqi Chen, Yiwen Yuan, Yuling Wu, Chak Tou Leong, Hanwen Du, Junchen Fu, Youhua Li, Jie Zhang. Chi Zhang, Li-jia Li, and Yongxin Ni. Video-bench: Human preference aligned video generation benchmark. arXiv preprint arXiv:xxx. 2024.
[16] Jonathan Ho, William Chan, Chitwan Saharia, Jay Whang, Ruiqi Gao, Alexey Gritsenko, Diederik Kingma, Ben Poole, Mohammad Norouzi, David Fleet, et al. Imagen video: High definition video generation with diffusion models. arXiv preprint arXiv:2210.0230: 2022.
[17] Jonathan Ho, Ajay Jain, and Picter Abbcel. Denoising diffusion probabilistic models. Advances in neural information processing systems, 33:6840-6851,, 6851, 2020.
[18] Anthony Hu. Lloyd Russell, Hudson Yeo, Zak Murez, George Fedoseev, Alex Kendall, Jamie Shotton. and Gianluca Corrado. Gaia-1: A generative world model for autonomous driving. arXiv preprint ar0iv:2309. 17080, 2023.
[19] Ziqi Huang. Yinan He, Jiashuo Yu. Fan Zhang, Chenyang Si, Yuming Jiang. Yuanhan Zhang, Tranxing Wu, Qingyang Jin, Nattapol Chanpaisit, et al. Vbench: Comprehensive benchmark suite for video generative models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 21807-21818 2024.
11

---

[20] Liu Jinxiu. Lin Shaoheng, Li Yinxiao, and Yang Ming-Hssun. Dynamicscaler: Seamless and scalable video generation for panoramic scenes, 2024.
[21] Yann LeCun. A path towards autonomous machine intelligence version 0.9. 2, 2022-06-27. Open Review, 62(1))1-62, 2022.
[22] Renjie Li, Panwang Pan, Bangbang Yang. Dejia Xu, Shijie Zhou, Xuanyang Zhang, Zeming Li, Achuta Kadambi, Zhangyang Wang, and Zhiwen Fan. 4k4dgen: Panoramic 4d generation at 4k resolution, 2024.
[23] Weiqi Li, Shijie Zhao, Chong Mou, Xuhan Sheng. Zhenyu Zhang, Qian Wang, Junlin Li, Li Zhang, and Jian Zhang. Omnidrag: Enabling motion control for omnidirectional image-to-video generation. arXiv preprint arXiv: 09623,, 2024.
[24] Shanchuan Lin, Ceyuan Yang. Hao He, Jianwen Jiang, Yuxi Ren, Xin Xia, Yang Zhao, Xuefeng Xiao, and Lu Jiang. Autoregressive adversarial post-training for real-time interactive video generation. arXiv preprint arXiv:2506. 09350, 2025.
[25] Rundong Luo, Matthew Wallingford, Ali Farhadi, Noah Snavely, and Wei-Chiu Ma. Beyond the frame: Generating 360 {[deg] panoramic videos from perspective videos. arXiv preprint wxXv:2500.077400, 2025.
[26] Alexander Quinn Nichol and Prafulla Dhariwal. Improved denoising diffusion probabilistic models. In International conference machine learning, pages 8162-8171. PMLR, 2021.
[27] William Peebles and Saining Xie. Scalable diffusion models with transformers. In Proceedings of the IEEE/CVF International Conference Computer Vision, pages 4195-4205, 2023.
[28] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Bjöm Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684-10695, 2022.
[29] Olaf Ronneberger, Philipp Fischer, and Thomas Brox U-net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-asisstd intervention-MICCA/ 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18, pages 234-241 Springer, 2015.
[30] Julian Schritwieser, Joannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt, Arthur Guez, Edward Lockhart, Demis Hassabis, Thore Graepel, et al. Mastering atari, go, chess and shogi by planning with a learned model. Nature, 588(78339):604--60)) 2020.
[31] David Silver. Aja Huang, Chris Maddison, Arthur Guez, Laurent Sifre, George Van Den Driessche, Julian Schrittwieser, loannis Antonoglou, Veda Panneershelvam, Mare Lanctot, et al. Mastering the game of go with deep neural networks and tree search. nature, 529(7587):844 489, 2016.
[32] Uriel Singer, Adam Polyak, Thomas Hayes, Xi Yin, Jie An, Songyang Zhang, Qiyuan Hu, Harry Yang. Oron Ashual, Oran Gafni, et al. Make-a-video: Text-to-video generation without text-video data. arXiv preprint arXiv:2209. 14792, 2022.
[33] Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International conference on machine learning, pages 2256 2265. PMLR, 2015.
[34] Jiaming Song. Chenlin Meng, and Stefano Ermon. Denoising diffusion implicit models. arXiv preprint 22Xiv:0010.02502. 2020.
[35] Jianlin Su, Murtadha Ahmed, Yu Lu, Shengfeng Pan, Wen Bo, and Yunfeng I Liu. Roformer: Enhanced transformer with rotary position embedding. Neurocomputing, 368:127063. 2024.
[36] Matthew Wallingford, Anand Bhattad, Aditya Kusupati, Vivek Ramanujan, Matt Deitke, Aniruddha Kembhavi, Roozbeh Mottaghi. Wei-Chin Ma. and Ali Farbadi. From an image to scene: Learning to imagine the world from million 360 videos. Advances in Neural Information Processing Systems, 37:17743-1776) 2024.
[37] Ang Wang, Baole Ai, Bin Wen, Chaojie Mao, Chen-Wei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, Jianyuan Zeng, Jiayu Wang, Jingfeng Zhang, Jingren Zhou, Jinkai Wang. Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang. Mengyang Feng. Ningyi Zhang, Pandeng Li. Pingyu Wu, Ruihang Chu, Ruili Feng. Shiwei Zhang, Siyang Sun. Tao Fang. Tianxing Wang. Tianyi Gui, Tingyu Weng. Tong Shen, Wei Lin, Wei Wang. Wei Wang, Wemmeng Zhou, Wente Wang, Wenting Shen, Wenyuan Yu. Xianzhong Shi, Xiaoming Huang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, Yijing Liu, Yiming Wang.
12

---

Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng. Yuntao Hong, Yupeng Shi, Yutong Feng. Zeyinzi Jiang, Zhen Han, Zhi-Fan Wu, and Ziyu Liu. Wan: Open and advanced large-scale video generative models. arXiv preprint urXiv:2503.203/ 2025.
[38] Qian Wang. Weiqi Li. Chong Mou, Xinhua Cheng, and Jian Zhang. 360dvd: Controllable panorama video generation with 360-degree video diffusion model. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 6913-6923, 2024.
[39] Zijian Zhang Rox Min Zuozhuo Dai Jin Zhou Jiangfeng Xiong Xin Li Bo Wu Jianwei Zhang Kathrina Wu Qin Lin Aladdin Wang Andong Wang Changlin Li Duojun Huang Fang Yang Hao Tan Hongmei Wang Jacob Song Jiawang Bai Jianbing Wu Jinbao Xue Joey Wang Junkun Yuan Kai Wang Mengyang Liu Pengyu Li Shuai Li Weiyan Wang Wenqing Yu Xinchi Deng Yang Li Yanxin Long Yi Chen Yutao Cui Yuanbo Peng Zhentao Yu Zhiyu He Zhiyong Xu Zixiang Zhou Zumnan Xu Yangyu Tao Qinglin Lu Songtao Liu Dax Zhou Hongfa Wang Yong Yang Di Wang Yuhong Liu Weijie Kong, Qi Tian and along with Caesar Zhong Jie Jiang. Hunyuanvideo: A systematic framework for large video generative models, 2024.
[40] Yuqing Wen, Yucheng Zhao, Yingfei Liu, Fan Jia, Yanhui Wang, Chong Luo, Chi Zhang, Tiancai Wang, Xiaoyan Sun, and Xiangyu Zhang. Panacea; Panoramic and controllable video generation for autonomous driving. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 6902-6912. 2024.
[41] Philipp Wu, Alejandro Escontrela, Danijar Hafner, Pieter Abbeel, and Ken Goldberg. Daydreamer: World models for physical robot learning. Conference on robot learning, pages 2226-2240. PMLR, 2023.
[42] Kevin Xie, Amirmojtaba Sabour, Jiahui Huang, Despoina Paschalidou, Greg Klar, Umar Iqbal, Sanja Fidler, and Xiaohui Zeng. Videopanda: Video panoramic diffusion with multi-view attention. arXiv preprint wrXiv:2504.1138, 2025.
[43] Zhuayi Yang, Jiayan Teng, Wendi Zheng, Ming Ding, Shiyu Huang, Jiazheng Xu, Yuanming Yang, Wenyi Hong, Xiaohan Zhang. Guanyu Feng, et al. Cogvideox: Text-to-vides diffusion models with an expert transformer. arXiv preprint arXiv:2408. 06072, 2024.
[44] Hong-Xing Yu, Haoyi Duan, Charles Herrmann, William T Freeman, and Jiajun Wu. Wonderworld: Interactive 3d seene generation from a single image. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 5916-5926, 5926, 2025.
[45] Cheng Zhang, Qianyi Wu, Camilo Cruz Gambardella, Xiaoshui Huang, Dinh Phung. Wanli Ouyang, and Jianfei Cai. Taming stable difffusion for text to 360 panorama image generation. In Proceedings of the IEEE/CVF Conference Computer Vision and Pattern Recognition, pages 6347-6357, 2024.
[46] Xuanlei Zhao, Xiaolong Jin, Kai Wang. and Yang You. Real-time video generation with pyramid attention broadcast. arXiv preprint arXiv:2408. 12588, 2024.
[47] Dian Zheng, Cheng Zhang, Xiao-Ming Wu, Cao Li, Chengfei Lv, Jian-Fang Hu, and Wei-Shi Zheng. Panorama generation from nfov image done right. arXiv preprint arXiv:2503. 18420, 2025.
[48] Wenzhao Zheng, Ruiqi Song, Xianda Guo, Chenming Zhang, and Long Chen. Genad: Generative end-to-end autonomous driving. In European Conference on Computer Vision, pages 87-104. Springer, 2024.
[49] Zheng Zhu, Xiaofeng Wang, Wangbo Zhao, Chen Min, Nianchen Deng, Min Dou, Yuqi Wang. Botian Shi, Kai Wang, Chi Zhang, et al. Is sora a world simulator? a comprchensive survey on general world models and beyond. arXiv preprint a40iv:2405 03520, 2024.
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: The claims stated in the abstract and introduction accurately reflect the paper's contributions and scope. They are aligned with both the theoretical foundations and the experimental results presented in the main body.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made the paper and important assumptions and limitations. A No or
NA answer to this question will not be perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings.
It is fine to include aspirational goals as motivation long is clear that these goals are not attained by the paper.
2. Limitations
Question:
Does the paper discuss the limitations the work performed by the authors?
Answer: [Yes]
The paper clearly outlines its limitations, including factors influencing the method's 's performance, demonstrating a responsible and transparent evaluation of its contri- butions.
Justification:
Guidelines:
The answer NA means that the paper has limitation while the answer No means that the paper has limitations, but those are not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g.. independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should articulated.
The authors should reflect on the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or speech-to-text system might not be used reliably provide closed captions for online lectures because it fails handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairesss.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, worse outcome might be that reviewers discover limitations that aren't acknowledged
the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
14

---

Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification: The paper's theoretical result provide the full set of assumptions but we do not propose any new theorems thus no proof needed.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated or referenced in the statement of any theorems.
The proof' can either appear in the main paper or the supplemental material, but if they appear in the supplemental material, the authors are encouraged provide short proof sketch to provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Does the paper fully disclose all the information needed to reproduce the main perimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)?
Question:
Answer: [Yes]
The paper includes all necessary implementation details, dataset informa- tion, and evaluation protocols ensure reproducibility of the key experimental findings supporting the main claims.
Justification:
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken make their results reproducible verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution be necessary to either make it possible for others to replicate the model with the same dataset, or provide access to model. In general. releasing code and data often one good way to accomplish this, but reproducibilityy can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of large language model), releasing of a model checkpoint, other means that are appropriate to the research performed.
specific model and empirical evaluation, may
While NeurlPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make it clear how reproduce that algorithm. (b) If the contribution is primarily the architecture clearly and fully.
model architecture, the paper should describe
(c) the contribution is a new model (c.g., a large language model), then there should either be way to access this model for reproducing the results or way to reproduce the model (e.g., with an open- open-source dataset or instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome describe the particular way they provide for reproducibiliity In the case of closed-source models, it may be that access to the model is limited in
15

---

some way (e.g., to registered users), but it should be possible for other researchers have some path reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
Answer: [No]
Justification: We are currently considering the possibility of open access to the data and code, but due to policy constraints, is not yet feasible make them publicly available. We will continue to evaluate this option as the project progresses.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurlPS code and data submission guidelines (https://nips.co
for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https for more details.
7 //nips //nips.cc/publlt wwtppppppcpc/guiee.pssppppppppee http://dddee/oeSuumiiiieeee (wooeeuwmisssooooolll.looooo
The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information as possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification: All essential training and testing -nnirauiionn-nlluniinn data splits, hyperpa- rameters, and optimization strategics- are thoroughly documented to support reproducibility and understanding.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Question:
Answer: [Yes]
Justification: The paper reports error bars and statistical significance metrics with clarity, detailing how they were calculated and what variability factors they represent.
Guidelines:
16

---

The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by enror bars, confi- dence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper.
The factors of variability that the error bars capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall
with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
It should be clear whether the error bar is the standard deviation the standard error of the mean.
is OK to report I-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of errors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error that would yield results that are out range (e.g. negative error rates).
If error bars reported in tables plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification: Detailed information on hardware used, compute time, and resource require- ments for experiments provided, allowing for practical replication of the results.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics
Answer: [Yes]
Justification: The research adheres to the NeurIPS Code of Ethics in all respects, ensuring ethical standards are met throughout the study.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special circumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., there a special consid- eration due to laws regulations in their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
17

---

Both the potential benefits and risks of the proposed work are discussed, including societal impacts and possible unintended consequences, along with considerations for mitigation.
Justification:
Guidelines:
The answer NA means that there no societal impact of the work performed.
If the authors answer NA or No, they should explain why their work has no societal impact why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g.. deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g.. pretrained language models, image generators, or scraped datasets)?
Answer: [Yes]
Justification:
The paper outlines measures to prevent misuse in section about social impacts.
Guidelines:
The answer NA means that the paper poses such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards is challenging, and many papers do not require this, but encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All third- party assets are properly cited with explicit mention of licenses and terms of use, demonstrating respect for intellectual property.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
18

---

The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g.. CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (e.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, pwwpsssipppsseeeee thcode com com/datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if it has changed) should be provided.
If this information not available online, the authors are encouraged to reach out to the asset's s creators.
13. New assets
Question: Are new assets introduced in the paper well documented and is the documentation provided alongside the assets?
Answer: [Yes]
Justification: New assets released as part of the paper are accompanied by comprehensive documentation detailing usage, licensing, and limitations.
Guidelines:
The answer NA means that the paper does not release assets.
Researchers should communicate the details the dataset/code/moodll as part their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification: The paper does not involve any form of crowdsoureing research involving human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
supplemental material is fine, but if the main contribu-
According to the NeurlPS Code of Ethics, workers involved in data collection, curation, or other labor should be paid at least the minimum wage in the country of the data collector.
15. Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or an equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
19

---

The study does not involve human subjects and thus does not require IRB or equivalent review.
Justification:
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with
human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent) may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere the NeurlPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
Declaration LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, formatting purposes and does not impact the core methodology. scientific rigorousness, or originality of the research, declaration is not required.
Answer: [Yes]
The paper clearly describes how LLMs are used as key, non-standard component in the proposed methodology.
Justification:
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, non-standard components.
Please refer to our LLM policy httpp://noorrps.cc//offrrrrcacce for what should or should not be described.
20

---

A Theory Analysis
Rotation equivariance in this paper means that rotating the spherical coordinates by an angle results in an identical rotation within the embedding space of the positional encoding- so the model perceives consistently "shifted" feature without altering the underlying structure. Therefore, rotation
establish rotation-equivariann equivariance, it suffices to show that any additive shift in longitude Ø, latitudeO, or time (as
defined in Eqs. ( and (2) induces an equivalent orthogonal rotation within the corresponding subspace of the encoding. We first recall the definitions from Eqs. ① and
7/ 2y 2 H
2x
0
1), Φ
π
1), 2
W
and for each frequency index
0, 1, N- 1,
Toos (20)) () cos( 12 ())
cos
sin (24)) )- cosl (24 ④
(2
cos (240) sin sin
72
sin(2 sin(2"
c2s(2
RoPEn
E R
(2 c2)(2%)) sin T2
sin(2"0) cos(24
sin 72
cos(240) sin sin sin(2
sin(240) sin(24 sin(2
The full positional embedding is the concatenation of all frequencies as [x,, y. - [RoPE (RoPE>,,,4. Ro E R
To prove that is equivariant under any rotation R (acting (Ir via the induced changes in (0,)) and any time shift + t + At, it suffices to show equivariance dimension-wiss. For clarity, we use an example. We begin by expressing each 8-dimensional
rotation in the longitude dimension block RoPEz,p,t.d in terms complex expoential cy = e²2 Co = 2 = c22 so that each entry of RoPEn.g,t.d is the real and imaginary part of product Cj 8 C with a, 3, 7 E {0,1) 1). In the complex domain, an additive shift, i.e. + Ø + Ád. corresponds to multipication by phase factor e Ad. This implies that each sin/cos pair involving i.e., (cos(24 AΦ), sin (2 (2))))) lies in a 2-dimensional subspace rotated by a 2x2 orthoganal matrix
(2s AΦ) s2n A
sin (2 sin(2"A@)) cos Am)
(2 Ad)
This rotation leaves the fand Icomponents unaffected expect for being scaled by fixed multiplicative factors, thus perserving equivariant within the full 8-dimension embedding. There are four such pairs in RoPE..g..e which results in an 8x8 block-diagonal orthogonal matrix T Stacking the matrices across all the frequencies 0, ....... yields global orthogonal matrix
T diaggTT T 88888N
which satisfies
S(R (I, f(x, f(I,
Here Rd denotes the rotation applied in longitude. The same reasoning applies to 0 and time where additive shifts similarly induce orthogonal transformations within their respective subspaces. Therefore, spatial rotations and temporal shifts are exactly mirrored by orthogonal rotations in the embedding space, confirming that the 3D spherical RoPE is rotation-equivariant.
B Efficiency Analysis
According to Table A our method achieves inference times comparable to both the base foundation model (despite operating at higher resolution) and to prior work such as 4K4DGen (while generating more frames). Currently, each generation step (covering ~3-5 seconds of video) takes approxi- mately 10-20 minutes on single A 1100 GPU. This latency stems from the high output resolution (2048x 1024) and the large size of the backbone models.
21

---

Model
Resolution
Inference Time
Inference Memory Usage
CogVideoXI.5-SB-I22
1360x x81
~ 16 min
29 GB
4k4DGen*
2048x 1024
16 min
212 GB
IaaW-IM
2048x 1024
~ (17xn) min
2(12xn) GB
laaW-CM
2048x 1024x40
17 min
212 GB
Table A: Analysis of video generation efficiency our method and several baselines.
As with most generative systems, there exists a trade-off between generation quality (e.g., resolution) and lateney. In this work, prioritize generation quality, though also discuss various optimization techniques that could accelerate inference as follow.
Multi-GPU Deployment: Utilizing FSDP or DeepSpeed Ulysses to parallelize inference across GPUs.
Model Compression and Acceleration: Techniques such as increasing the VAE encoding
granularity-e. e.g. encoding larger spatial chunks as in LTX- 12 can significantly
reduce computational cost.
Efficient Attention Mechanisms: Incorporating architectural improvements such as Pyramid Attention Broadcast (PAB) [46] can help accelerate DiT-based video generation.
Autoregressive Frame Scheduling: Reducing the number of frames generated at each step and progressively extending sequences in autoregressive fashion (as explored in recent work like AAPT (24)) may enable near-real-time inference with minimal quality compromise.
C Failure Case Analysis
In terms of performance on highly complex scenes, such urban street views, we find that our model is less reliable compared to natural or less cluttered environments. For instance, in one case involving an aerial view of a busy urban street with numerous cars, the generated video exhibited unnatural behavior: some cars remained static while others moved in inconsistent physically implausible directions. There are two main contributing factors:
1. Model Capacity: The base models we built on (including CogVideoX and other comparable open-source video diffusion models) struggle to robustly handle scenarios with multiple independently moving objects, which exceeds the temporal modeling capacity of existing models.
2. Training Data Bias:
To ensure visual stability, we filtered out videos with significant camera shake many of which were hand-held recordings containing dense motion and multiple objects. As a result, the training set is less representative of such complex dynamic environments, which impacts generalization.
We have also conducted preliminary failure case analysis and identified several common failure modes
1. Complex Motion Scene Crowding: Scenes with high density of independently moving objects (e.g.. vehicles, pedestrians) often lead to degraded performance. The base model's capacity to process multiple interacting objects attention is limited, resulting in static or erratically moving elements.
2. Human Actions:
When humans are present in the scene, motion may be unrealistic or static. This is partly due to the difficulty of modeling articulated human motion in video diffusion models, and further exacerbated in 360-degree video due varying perspective motion distortions across the sphere.
3. Unsuited Input Scenarios: Close-up uuse-up views of objects, animals, or plants often result in implausible generations-sutt as oversized elements or distorted layouts due to the egocentric nature of 360-degree video. When the initial view covers a very narrow or zoomed- in area, the extrapolated scene tends to resemble "Lilliput effect", where everything appears disproportionately large. We find that the laaW works best for wide-fiele field scenes like indoor rooms, landscapes, acrial views, where surrounding context is available.
22

---

Parameters
Value
Video Height
1024
Video Width
2048
Video Frames
49
Batch Size
Mixed Precision
6
Optimizer
Adam W
Optimizer Betas
(0.9, 0.95)
Optimizer Weight Decay
le-4
Learning Rate
2e-5
Warmup Steps
100
Inference Steps
25
Frame Per Second
16
Masks Number
Table B: Hyperparameters setting of experiments.
Left View
Front View
Right View
Top View
Back View
Bottom View
Figure A: Different view masks setup in our method.
D Experiments Setup
present the hyperparameters used in our experiment in Table B The same hyperparameters are applied to both laaW-IM and IaaW-MM, and pipeline is built upon the Cog VideoX codebase.
We also present our masks setup in Fig. A which contains six perspectives of one panoramic image/video and these can together seamlessly reconstruct the full panoramic scene.
E More Visualization Results
We present a qualitative visualization comparison between WonderWorld [44] and our laaW in Fig B The results find that our method generate panoramic dynamic world instead of single static 3D world scene compared with WonderWorld.
We present additional visualization results complex scenes and indoor scenes in Fig. which demonstrate that our method exhibits significant diversity across various scenarios.
We also present additional visualization results of our world initialization Fig. D
23

---

Input Image
SD Scone by WonderWorrd
Panoramic Video by laaW
Figure B: Qualitative visualization comparison between WonderWorld and our laaW.
Input Image
Generated resutts with complex
Input Image
Generated results with indoor
Figure C: Qualitative visualization on complex scenes and indoor scenes.
Input Image
World Initialization Results
Figure D: More visualization results of world initialization.
24


Reviews:
[{"review_id": "fbASTDyidA", "reviewer": "Reviewer_GNKG", "summary": "This 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.", "questions": "1. 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?", "limitations": "N/A", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "After reading the rebuttal, my concerns have been resolved. I still find this work interesting and promising, and I am therefore inclined to recommend acceptance.", "rebuttal": "We sincerely appreciate the reviewer’s recognition of our contributions, particularly the technical innovations in both the overall pipeline and the proposed 3D Spherical RoPE. Below, we address the reviewer’s concerns and questions in detail.\n> 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\nThank you for your insightful comment. We agree that demonstrating the model's performance across a broader and more diverse set of scenes would strengthen the paper. Although we are unable to provide additional visualizations during the rebuttal phase according to the policy, we will include more results on diverse scenarios—including indoor and complex scenes—in the final version of the paper and supplemental material.\n\nRegarding performance on indoor environments, our model performs reasonably well. This is partly because a substantial portion of our training data includes game-rendered equirectangular videos, many of which are indoor scenes with consistent structure and lighting.\n\nHowever, performance on complex scenes with many moving objects is more limited. For example, in a case involving an aerial view of urban streets with a large number of cars, the generation results are not satisfactory: some cars remain static while others move in unnatural or incorrect directions. There are two main contributing factors:\n\n1. Model Capacity: The base models we built on (including CogVideoX and other comparable open-source video diffusion models) struggle to robustly handle scenarios with multiple independently moving objects, which exceeds the temporal modeling capacity of existing models.\n2. Training Data Bias: To ensure visual stability, we filtered out videos with significant camera shake—many of which were hand-held recordings containing dense motion and multiple objects. As a result, the training set is less representative of such complex dynamic environments, which impacts generalization.\n\nWe acknowledge this limitation and plan to enhance both the model capacity and data diversity in future work to better handle these challenging scenarios.\n\n> 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\nWe appreciate the reviewer’s insightful comment. While the World Continuation stage does leverage the IaaW-VLM to automatically generate prompts for scene continuation, it is important to clarify that this stage can indeed be guided by explicit user instructions. The VLM is introduced to automate the repeatedly continuation process, but user-provided prompts can always override or guide the generation pipeline when desired. However, we acknowledge that the current model exhibits relatively limited instruction-following ability, especially when prompted with very short commands. This limitation arises because the training captions were generated using an automatic captioning model and were not specifically curated or aligned with diverse forms of user intent. We thank the reviewer for pointing out this issue. In future work, we plan to enhance the model’s instruction-following capabilities by incorporating more instruction-following captions, as well as refining the prompt processing method to better support flexible user control.\n\n> 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\nThank you for this insightful question. We have conducted several preliminary trials with user-specified instructions such as \"move towards the mountains.\" While our model can follow such commands to some extent, the behavior is not entirely reliable or consistent. This limitation primarily stems from the nature of our training data: the captions used for training were automatically generated and not explicitly curated to reflect diverse or instruction-driven user intents. As a result, the model lacks strong grounding in flexible, user-controlled language commands. In future work, we plan to enhance the model’s instruction-following capabilities by incorporating more instruction-following captions, as well as refining the prompt processing method to better support flexible user control.\n\n> About long-term consistency: what is the maximum length of the generated video in practice?\n\nThank you for your interest in this aspect of our work. In practice, we have observed that performing world exploration and world continuation for around 5 to 10 rounds repeatedly can maintain reasonable quality. Specifically, with 10 rounds, we are able to generate a video approximately one minute long. However, when we extend the video beyond 10 rounds, the quality tends to degrade significantly. This degradation is primarily due to cumulative errors that arise during long-term generation, which is a known challenge in the field. The main limitation preventing longer extensions is that, in our current setup, generation relies solely on the most recent chunk due to memory constraints. To enhance long-term consistency, we are exploring techniques to better leverage historical frames—such as compressing them and incorporating as much of the historical context as possible during generation. We will include a discussion of this phenomenon and potential solutions in the revised version of the paper."}, {"review_id": "YQdA2h51yW", "reviewer": "Reviewer_fetS", "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.", "questions": "Kindly refer to the [Weaknesses].", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "### Strengths\n* This work presents a groundbreaking world generation model capable of producing high-quality dynamic panoramas and supporting interactive experiences from a single image.\n* The motivation behind the design of IaaW is well-founded and compelling, with its contributions validated through comparisons against FETA and the 360I2V baseline.\n* The paper is well-written and easy to follow.\n### Weaknesses\n* 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.\n* 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).\n* 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.", "was_revised": true, "final_justification": "The authors' response and additional experiments have addressed my concerns. Considering the perspectives of the other reviewers, I will maintain my current score.", "rebuttal": "We greatly appreciate the reviewer’s recognition of our contributions. Below, we provide detailed responses to the reviewer’s concerns and questions.\n\n> 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.\n\nThank you for the insightful suggestion. We will include additional 3D positional encoding methods for comparison in the revised manuscript. Our proposed 3D spherical RoPE extends traditional 3D rotary embeddings to 4D by modulating angular and temporal components, which significantly enhances the structure generation and temporal consistency of panoramic video. \n\nWe also appreciate the suggestion to provide a theoretical proof of the rotation equivariance of our method. We will include the following proof in the appendix of the revised manuscript. \n\n### Proof of Rotation Equivariance\n\nRotation equivariance in this paper means that rotating the spherical coordinates by an angle results in an identical rotation within the embedding space of the positional encoding—so the model perceives a consistently \"shifted\" feature without altering the underlying structure. Therefore, to establish rotation-equivariance, it suffices to show that any additive shift in longitude $\\phi$, latitude $\\theta$, or time $t$ (as defined in Equations 1 & 2) induces an equivalent orthogonal rotation within the corresponding subspace of the encoding.\n  We first recall the definitions from Equations 1 & 2:\n  $$\\theta = \\frac{\\pi}{2}(\\frac{2y}{H}-1), \\phi = \\pi(\\frac{2x}{W}-1), \\tau = 2 \\pi t,$$\n  and for each frequency index $d=0,1,...,N-1,$ \n$$\\text{RoPE}\\_{x,y,t,d}=\\left[ \\begin{matrix} \n    \\cos(2^d \\theta) \\cdot \\cos(2^d \\phi) \\cdot \\cos(2^d \\tau) \\\\\\\\\n    \\sin(2^d \\theta) \\cdot \\cos(2^d \\phi) \\cdot \\cos(2^d \\tau) \\\\\\\\\n    \\cos(2^d \\theta) \\cdot \\sin(2^d \\phi) \\cdot \\cos(2^d \\tau) \\\\\\\\\n    \\sin(2^d \\theta) \\cdot \\sin(2^d \\phi) \\cdot \\cos(2^d \\tau) \\\\\\\\\n    \\cos(2^d \\theta) \\cdot \\cos(2^d \\phi) \\cdot \\sin(2^d \\tau) \\\\\\\\\n    \\sin(2^d \\theta) \\cdot \\cos(2^d \\phi) \\cdot \\sin(2^d \\tau) \\\\\\\\\n    \\cos(2^d \\theta) \\cdot \\sin(2^d \\phi) \\cdot \\sin(2^d \\tau) \\\\\\\\\n    \\sin(2^d \\theta) \\cdot \\sin(2^d \\phi) \\cdot \\sin(2^d \\tau) \\\\\\\\\n\\end{matrix}\n  \\right] \\in \\mathbb{R}^8$$\nThe full positional embedding is the concatenation of all frequencies as \n$$f(x,y,t)=\\left[ \\text{RoPE}\\_{x,y,t,0} || \\text{RoPE}\\_{x,y,t,1} || \\text{RoPE}\\_{x,y,t,2} || ...\\right] \\in \\mathbb{R}^{8N}$$\nTo prove that $f$ is equivariant under any rotation $R$ (acting on $(x,y)$ via the induced changes in $(\\theta,\\phi)$) and any time shift $t\\rightarrow t+\\Delta t$, it suffices to show equivariance dimension-wise. For clarity, we use a rotation in the longitude dimension $\\phi$ as an example. \n\nWe begin by expressing each 8-dimensional block $\\text{RoPE}\\_{x,y,t,d}$ in terms of complex expoential\n $c_\\theta=e^{i2^d\\theta}$, $c_\\phi=e^{i2^d\\phi}$, $c_\\tau=e^{i2^d\\tau}$,\nso that each entry of $\\text{RoPE}\\_{x,y,t,d}$ is the real and imaginary part of a product $c^\\alpha_\\theta c^\\beta_\\phi c^\\gamma_\\tau$ with $\\alpha,\\beta,\\gamma \\in \\\\{0,1\\\\}$. \nIn the complex domain, an additive shift, i.e.$\\phi \\rightarrow \\phi+\\Delta\\phi$, corresponds to multipication by a phase factor $e^{i2^d\\Delta\\phi}$. This implies that each sin/cos pair involving $\\phi$, i.e., $(\\cos(2^d \\Delta \\phi),\\sin(2^d \\Delta \\phi))$, lies in a 2-dimensional subspace rotated by a 2x2 orthoganal matrix $R^\\phi\\_d(2^d\\Delta\\phi)=( \\begin{matrix} \n    \\cos(2^d \\Delta \\phi) & -\\sin(2^d \\Delta \\phi) \\\\\\\\\n    \\sin(2^d \\Delta \\phi) & \\cos(2^d \\Delta \\phi)\n\\end{matrix})$. \nThis rotation leaves the $\\theta$ and $t$ components unaffected expect for being scaled by fixed multiplicative factors, thus perserving equivariant within the full 8-dimension embedding. \n\nThere are four such pairs in $\\text{RoPE}\\_{x,y,t,d}$, which results in an 8x8 block-diagonal orthogonal matrix $T^\\phi_d$. Stacking the matrices across all the frequencies $d=0,...,N-1$ yields a global orthogonal matrix \n$T^{\\phi}=diag(T^\\phi_0,...,T^\\phi_{N-1})\\in \\mathbb{R} ^{8N \\times 8N}$，which satisfies $f(R^\\phi \\cdot (x,y,t))=T^\\phi\\cdot f(x,y,t)$.\n\nHere $R^\\phi$ denotes the rotation applied in longitude. The same reasoning applies to $\\theta$ and time $\\tau$, where additive shifts similarly induce orthogonal transformations within their respective subspaces. Therefore, spatial rotations and temporal shifts are exactly mirrored by orthogonal rotations in the embedding space, confirming that the 3D spherical RoPE is rotation-equivariant.\n\n> 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).\n\nThank you for pointing this out. We agree that comparisons at the world initialization and world continuation stages with recent relevant methods are valuable, and we appreciate the suggestions. Unfortunately, OmniDrag has not released its code, so we were unable to include a direct comparison. \n\n| Models  | SC-View | SC-All | MS-View | MS-All | AQ-View | AQ-All |\n| ------- | ------- | ------ | ------- | ------ | ------- | ------ |\n| 360I2V  | 92.5    | 94.8   | 98.9    | 98.7   | 49.5    | 55.0   |\n| 4K4DGen | 94.1    | 95.1   | 99.2    | 98.8   | 46.0    | 53.4   |\n| IaaW-CM | **95.8**    | **97.2**   | **99.3**    | **99.2**   | **49.7**    | **55.7**   |\n\nFor 4K4DGen, which was proposed for animating 4K panoramic images, its code was not publicly available prior to our submission deadline, which prevented us from including a comparison at that time. Now that it is accessible, we have conducted an additional comparison with our IaaW-CM as below, focusing specifically on the world continuation stage. A direct comparison in the world initialization stage was not feasible due to differences in input format and task formulation. The results show that our model outperforms 4K4DGen in terms of both temporal consistency and visual quality. We attribute this to two factors:\n1. Our model is trained on a large corpus of native equirectangular videos, allowing it to model the panoramic space holistically, including its inherent geometric distortions. This enables better spatial coherence across views.\n2. In contrast, 4K4DGen divides the panoramic image into multiple blocks and animates them independently using a relatively weak image-to-video model (SVD), without any textual control. This process often results in spatial artifacts, temporal discontinuity, and semantic misalignment across blocks.\n\nWe also thank the reviewer for mentioning WonderWorld, which we had not considered previously. WonderWorld focuses on static 3D scene generation using Gaussian-Splatting-like representation from a single image. While it does not support dynamic video generation as our method does, we will include a qualitative comparison in the revised version with our IaaW-IM in world initialization stage. Notably, WonderWorld does not produce equirectangular video but instead reconstructs a pseudo-3D splatting-based scene, which may enable longer exploration paths but often suffers from artifacts inherent to splatting (e.g., blur and point cloud sparsity). In contrast, our method generates temporally consistent and spatially coherent dynamic video with a spherical field of view, offering a more immersive and artifact-free experience.\n\n> 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.\n\nThank you for this valuable comment, and we fully agree that efficiency is a key factor for practical deployment of any candidate pipeline for interactive visual world modeling. We report the efficiency of our pipeline as follows.\n\n| Model                     | Resolution   | Inference Time       | Inference Memory Usage    |\n| ------------------------- | ------------ | -------------------- | ------------------------- |\n| CogVideoX1.5-5B-I2V(base) | 1360\\*768\\*81  | ~16 min              | $\\ge $9GB               |\n| 4k4DGen\\*                  | 2048\\*1024\\*14 | ~16 min              | $\\ge $12GB              |\n| IaaW-IM                   | 2048\\*1024\\*49 | ~(17*n) min n=\\#views | $\\ge $(12*n)GB n=\\#views |\n| IaaW-CM                   | 2048\\*1024\\*49 | ~17 min              | $\\ge $12GB              |\n\n$\\small{\\text{4k4DGen with \\* here represents it can generate 4096*2048 resolution video, while for comparison, we include the 2048\\*1024 results here.}}$\n\nOur method achieves inference times comparable to both the base foundation model (despite operating at higher resolution) and to prior work such as 4K4DGen (while generating more frames). Currently, each generation step (covering ~3–5 seconds of video) takes approximately 10–20 minutes on a single A100 GPU. This latency stems from the high output resolution (2048×1024) and the large size of the backbone models. As with most generative systems, there exists a trade-off between generation quality (e.g., resolution) and latency. In this work, we prioritize generation quality, though we also discuss various optimization techniques that could accelerate inference in our response to Reviewer 4SKk."}, {"review_id": "fl9nNM5PRE", "reviewer": "Reviewer_8ork", "summary": "This 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.", "questions": "\"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.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 3, "originality": 3, "strengths_and_weaknesses": "The 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.", "was_revised": true, "final_justification": "The authors have provided a good point-by-point rebuttal to the review, hence I decided to increase my rating of the submission.", "rebuttal": "We greatly appreciate the reviewer's recognition of the challenging nature of 4D world generation and the innovative value of our approach. We appreciate your recognition of our use of panoramic video as a 4D representation, the novelty of our unified spherical position embedding, and the relevance of adapting panoramic spatial modeling to temporal progression. Below, we provide detailed responses to the reviewer’s concerns and questions.\n\n> Fig.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\nIn fact, the MMDiT block is indeed modified in our approach. As shown in Fig. 3, we introduce an additional view-aware branch, highlighted by the grey background. This newly added branch operates in parallel with the original text + vision stream. Both the original and the new branches apply 3D full attention independently. The original branch processes the standard text + vision input, while the added branch incorporates view features by taking auxiliary view prompts, applying corresponding spatial masks, and performing 3D full attention over the masked vision regions. The outputs from both branches are then fused through a gated mechanism to produce the final output. We will revise Section 3.1.1 to explicitly state that the MMDiT architecture is modified with an additional view-aware path and clarify the roles of each attention path to better align with Fig.3.\n\n> Also, 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\nThank you for pointing this out. We agree that our current version lacks an explicit and clear explanation of the view masks and view prompts, and we will address this in the revised manuscript. In the World Initialization stage, we introduce a multi-view composition mechanism to enhance spatial diversity and controllability. This mechanism takes two inputs:\n- View masks: binary masks that correspond to user-specified predefined views (e.g., front, left, top). The types of masks are illustrated in supplementary material.\n- View prompts: natural language prompts aligned with each masked region generated by our IaaW-VLM, providing textual guidance for content generation from that specific viewpoint.\n\nThis setup allows the system to generate a more diverse and spatially view-aware initial world. We will revise Sections 3.1.1 and 3.2.1 to explain this clearer.\n\n> Section 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\nThank you for pointing this out and we appreciate the opportunity to clarify. The IaaW-VLM module is designed to handle two distinct scenarios, as illustrated by the two input-output pathways in Fig. 4. It is employed in both the World Initialization and World Continuation stages, taking an image and a video as input, respectively. Specifically, IaaW-VLM is fine-tuned for two types of inputs: (1) an image input used during the World Initialization stage, and (2) a video input used during the World Continuation stage. In the initialization stage, the original input image is the starting point. IaaW-IM first processes this image to produce an initial equirectangular video prompt $P\\_1$, as well as a set of optional view prompts $P\\_v$ to support multi-view composition. In the continuation stage, IaaW-VLM then takes the previously generated video as input to produce the next video prompt for further progression. We will clarify this distinction more explicitly and split the figure for the two stages in the revised version.\n\n> It is unclear why the VLM itself is so important for this task. \n\nThank you for pointing this out and we appreciate the opportunity to clarify. VLM used in our method plays a distinct but crucial role in providing textual information across world initialization stage and world continuation stage. \nIn the World Initialization stage, the VLM takes a single-view input image and generates:\n- A holistic video prompt capturing the overall scene evolution.\n- Optional view prompts, each aligned with a predefined view mask (e.g., front, left), enabling multi-view composition with spatial grounding.\n\nIn the World Continuation stage, the VLM conditions on the previously generated (and rotated) video and produces the next-step prompt, guiding coherent temporal progression.\n\nBy serving as the bridge between visual context and prompt-based video generation, the VLM allows our system to automatically create an extensible and interactive 360-degree world from just a single image. We will revise the manuscript to clarify this mechanism and its importance.\n\n> The 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\nThank you for your feedback. We have double-checked the supplementary material and verified that all videos, including the exploration stage, play correctly on our end, includes Photos, Media Player, and PotPlayer in Windows 11.\n\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.\" Not 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\nThank you for the feedback. Our intention here is to highlight a key distinction: traditional world models typically predict the next state conditioned on actions—i.e., they model action-to-state transitions step by step. In contrast, once our immersive world is synthesized, users can explore it freely via arbitrary viewpoint changes—without requiring step-by-step action conditioning. This is enabled by the inherently 360-degree, rotatable nature of the generated video, and shown in demo video of supplemental materials. We will revise the sentence to better clarify this distinction."}, {"review_id": "0Q0EefYjAH", "reviewer": "Reviewer_4SKk", "summary": "This 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.", "questions": "1) 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?", "limitations": "The 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.", "rating": 2, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths: \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.", "was_revised": true, "final_justification": "I have carefully read the authors' responses to my concerns and comments, as well as the interactions between the authors and all reviewers. I think some of reviewer concerns have not been satisfactorily resolved yet. I think this paper still needs further improvement by resolving these unresolved concerns. Therefore, in my opinion, it does not yet meet the standards required by the conference.", "rebuttal": "We greatly appreciate the reviewer’s recognition of our contributions. Below, we provide detailed responses to the reviewer’s concerns and questions.\n\n> The paper contributes primarily through engineering and system design rather than theoretical innovation. While practical frameworks are valuable, this limits its broader scientific impact. How to theoretically demonstrate the effectiveness of the method proposed in the paper?\n\nWhile our panoramic video generation pipeline may appear engineering-oriented at first glance, each stage is carefully designed based on theoretically analysis and reflects an optimal solution to its specific subproblem. Although our work does not claim purely theoretical innovation, the overall system is built upon principled reasoning rather than engineering convenience or performance tuning, addressing the fundamental ambiguities of visual world generation. \n\n1. Stage 1: Single-view to spherical dynamics\n\n    This stage tackles the underdetermined task of reconstructing 360° motion from a single static image. We formulate it as an ill-posed inverse problem and reduce ambiguity through strong priors: spherical RoPE for rotational symmetry and multi-view composition for consistency and diversity. These components constrain the solution space and enable plausible world reconstruction from sparse input.\n\n2. Stage 2: View-consistent rotation\n\n    This stage addresses the challenge of generating view-consistent panoramic video under camera motion. We design a group-equivariant rotation module that ensures latent representations transform coherently with spherical rotations. By leveraging equivariance theory, we guarantee that changes in viewpoint result in structured transformations in the embedding space. This property is validated both theoretically and empirically.\n\n3. Stage 3: Time-consistency generation\n\n    This stage ensures long-range temporal consistency in panoramic video generation. By conditioning on embeddings from previous chunks, we reduce visual drift and maintain coherent scene dynamics over time. Our design leverages diffusion convergence insights to stabilize generation across successive segments.\n\nThese stages form a principled and interpretable solution to the problem of extending a static view into an immersive, controllable, and coherent video world—bridging perception, geometry, and generation under a unified theoretical framework. We really appreciate this comment and will include corresponding discussion to provide additional insights in the revised version. \n\n> Generation of 2048×1024 videos in three stages on multiple A100 GPUs over several weeks limits practical applicability; \n\nThank you for raising this important concern. While training was indeed resource-intensive, we believe the practical applicability remains reasonable for two key reasons:\n1. The multi-week duration refers only to model training. Once trained, only the inference stage is required for user applications, which is significantly faster and more scalable.\n2. The long training time was primarily due to limited hardware availability. With access to larger compute clusters, the process can be accelerated to days or even hours. Moreover, several inference-time optimizations can further improve runtime and deployment efficiency, which will be discussed in the next part.\n\n> No runtime or resource‑efficiency analysis is provided. 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\nThank you for raising this important point. We agree that runtime and efficiency are critical for assessing practical applicability, and we will include detailed analysis in the revised version. We present our inference time statistics as below.\n\n| Model                     | Resolution   | Inference Time       | Inference Memory Usage    |\n| ------------------------- | ------------ | -------------------- | ------------------------- |\n| CogVideoX1.5-5B-I2V(base) | 1360\\*768\\*81  | ~16 min              | $\\ge $9GB               |\n| 4k4DGen\\*                  | 2048\\*1024\\*14 | ~16 min              | $\\ge $12GB              |\n| IaaW-IM                   | 2048\\*1024\\*49 | ~(17*n) min n=\\#views | $\\ge $(12*n)GB n=\\#views |\n| IaaW-CM                   | 2048\\*1024\\*49 | ~17 min              | $\\ge $12GB              |\n\n$\\small{\\text{4k4DGen with \\* here represents it can generate 4096*2048 resolution video, while for comparison, we include the 2048\\*1024 results here.}}$\n\nIt can be observed that our method achieves inference times comparable to both the base foundation model (despite operating at higher resolution) and to prior work such as 4K4DGen (while generating more frames). Currently, each generation step (covering ~3–5 seconds of video) takes approximately 10–20 minutes on a single A100 GPU. This latency stems from the high output resolution (2048×1024) and the large size of the backbone models. Current real-time methods often use lower resolution and smaller models, which trade off generation quality compared to our approach.\n\nWe acknowledge that this inference time limits real-time deployment and we are actively exploring several optimization directions:\n1. Multi-GPU Deployment: Utilizing FSDP or DeepSpeed Ulysses to parallelize inference across GPUs.\n2. Model Compression and Acceleration: Techniques such as increasing the VAE encoding granularity—e.g., encoding larger spatial chunks as in LTX-Video[1]—can significantly reduce computational cost.\n3. Efficient Attention Mechanisms: Incorporating architectural improvements such as Pyramid Attention Broadcast (PAB) [2] can help accelerate DiT-based video generation.\n4. Autoregressive Frame Scheduling: Reducing the number of frames generated at each step and progressively extending sequences in an autoregressive fashion (as explored in recent work like AAPT[3]) may enable near-real-time inference with minimal quality compromise.\n\n[1]:HaCohen, Yoav, et al. \"Ltx-video: Realtime video latent diffusion.\" arXiv preprint arXiv:2501.00103 (2024).\n\n[2]:Zhao, Xuanlei, et al. \"Real-time video generation with pyramid attention broadcast.\" arXiv preprint arXiv:2408.12588 (2024).\n\n[3]:Lin S, Yang C, He H, et al. Autoregressive Adversarial Post-Training for Real-Time Interactive Video Generation[J]. arXiv preprint arXiv:2506.09350, 2025.\n\n> The continuation stage conditions only on the most recent chunk, leading to drift over very long sequences, as admitted in the Limitations section.\n\nThank you for your valuable feedback. As acknowledged in the Limitations section, our current continuation stage conditions only on the most recent chunk, which can lead to drift over very long sequences. This design choice is primarily due to memory constraints, which prevent us from incorporating a longer history during generation. However, we are actively exploring strategies to better utilize historical frames—for example, by compressing them or designing mechanisms that allow us to integrate as much historical context as possible within available memory. We believe such improvements can significantly enhance long-term consistency in future iterations.\n\n> 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\nThank you for this important question. We are unable to provide additional visualizations or failure case examples during the rebuttal phase, but we will include a detailed section with such cases in the revised version of the paper and supplementary materials.\n\nIn terms of performance on highly complex scenes, such as urban street views, we find that our model is less reliable compared to natural or less cluttered environments. For instance, in one case involving an aerial view of a busy urban street with numerous cars, the generated video exhibited unnatural behavior: some cars remained static while others moved in inconsistent or physically implausible directions. These limitations are primarily due to the challenges of modeling complex, multi-object motion—an area where current video diffusion backbones (including CogVideoX and comparable open-source models) still struggle, due to limited temporal modeling capacity for dense and heterogeneous motion.\n\nWe have conducted a preliminary failure case analysis and identified several common failure modes:\n1. Complex Motion or Scene Crowding: Scenes with a high density of independently moving objects (e.g., vehicles, pedestrians) often lead to degraded performance. The base model's capacity to process multiple interacting objects attention is limited, resulting in static or erratically moving elements.\n2. Human Actions: When humans are present in the scene, motion may be unrealistic or static. This is partly due to the difficulty of modeling articulated human motion in video diffusion models, and further exacerbated in 360-degree video due to varying perspective motion distortions across the sphere.\n3. Unsuited Input Scenarios: Close-up views of objects, animals, or plants often result in implausible generations—such as oversized elements or distorted layouts—due to the egocentric nature of 360° video. When the initial view covers a very narrow or zoomed-in area, the extrapolated scene tends to resemble a “Lilliput effect,” where everything appears disproportionately large. We find that the IaaW works best for wide-field scenes like indoor rooms, landscapes, or aerial views, where surrounding context is available."}]

Author rebuttal:

Meta-review: This paper presents a three-stage framework for turning a single image into an interactive, temporally extendable 360° world, with a well-motivated 3D Spherical RoPE, clear system design and demo views. GNKG and fetS are positive; 8ork raised clarity issues but increased the score to accept after the rebuttal. Reviewer 4SKk raised a few additional concerns after the rebuttal, but these were not posed to the authors for further reply. Upon careful review, the AC finds these points to be minor, and in several cases contradictory to other reviewers’ assessments. Therefore, AC recommend acceptance.
Decision: Accept (poster)