FantasyWorld: Geometry-Consistent World Modeling via Unified Video and 3D Prediction
Authors: Yixiang Dai
Fan Jiang
Chiyu Wang
Mu Xu
Yonggang Qi
Venue: ICLR 2026 Poster (2026)
Keywords: World Model
Video Generation
3D Genearation
3D-aware video generation
Primary area: generative models
TL;DR: FantasyWorld unifies video priors with geometric grounding in a feed-forward dual-branch model that emits video and 3D features in one pass, producing 3D-consistent worlds without per-scene optimization.

Abstract: High-quality 3D world models are pivotal for embodied intelligence and Artificial General Intelligence (AGI), underpinning applications such as AR/VR content creation and robotic navigation. 
Despite the established strong imaginative priors, current video foundation models lack explicit 3D grounding capabilities, thus being limited in both spatial consistency and their utility for downstream 3D reasoning tasks. 
In this work, we present FantasyWorld, a geometry-enhanced framework that augments frozen video foundation models with a trainable geometric branch, enabling joint modeling of video latents and an implicit 3D field in a single forward pass. 
Our approach introduces cross-branch supervision, where geometry cues guide video generation and video priors regularize 3D prediction, thus yielding consistent and generalizable 3D-aware video representations. 
Notably, the resulting latents from the geometric branch can potentially serve as versatile representations for downstream 3D tasks such as novel view synthesis and navigation, without requiring per-scene optimization or fine-tuning. 
Extensive experiments show that FantasyWorld effectively bridges video imagination and 3D perception, outperforming recent geometry-consistent baselines in multi-view coherence and style consistency. 
Ablation studies further confirm that these gains stem from the unified backbone and cross-branch information exchange.

Published as a conference paper at ICLR 2026
FANTASYWORLD: GEOMETRY-CONSISTENT WORLD MODELING VIA UNIFIED VIDEO AND 3D PREDICTION
Yixiang Dai
Fan Jiang" Chiyu Wang", Mu Xu', Yonggang Qi
AMAP, Alibaba Group
Beijing University of Posts and Telecommunications
salyixiangf66@@mai1
{frank. jf, wangchiyu. wey, xmmuxx/\ea1lbabbb-nc.comm giyg@bupt. edu.cn
ABSTRACT
High-quality 3D world models are pivotal for embodied intelligence and Artificial General Intelligence (AGI), underpinning applications such as AR/VR content creation and robotic navigation. Despite the established strong imaginative pri- ors, current video foundation models lack explicit 3D grounding capabilities, thus being limited in both spatial consistency and their utility for downstream 3D rea- soning tasks. In this work, we present FANTASY W WORLD, geometry-enhanee framework that augments frozen video foundation models with a trainable geo- metric branch, enabling joint modeling of video latents and an implicit 3D field in a single forward pass. Our approach introduces cross-brance supervision, where geometry cues guide video generation and video priors regularize 3D prediction, thus yielding consistent and generalizable 3D-aware video representations. No- tably, the resulting latents from the geometric branch can potentially serve ver- satile representations for downstream 3D tasks such as novel view synthesis and navigation, without requiring per-scene optimization fine-tuning fine-tuning. Extensive ex- periments show that FANTASY W WORLD effectively bridges video imagination and 3D perception, outperforming recent seometry-consistene baselines in multi-view coherence and style consistency. Ablation studies further confirm that these gains stem from the unified backbone and cross-branch information exchange. Project page: https: /ota:://Eantsss amap. github. io/ cantasy-worlde
OntesyWort
Figure I: FANTASYWORK overview. Given multimodal inputs (image, text, and camera trajec- tory), the model generates photorealistic videos along the specified views while constructing an implicit 3D representation for consistent geometry.
"Equal contribution.
Project leader.
*Corresponding author.
Work done during internship AMAP, Alibaba Group.

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Published as a conference paper at ICLR 2026
1 INTRODUCTION
Building world models has long been viewed as a key step toward Artificial General Intelligence (AGI). By modeling environments, objects, causal relations, and temporal dynamics, world models enable agents to predict, plan, and generate, endowing them with human-like understanding and creative abilities. At the core of this vision lies the construction of high-quality, diverse 3D envi- ronments, which support wide spectrum of applications spanning AR/VR content creation, robotic navigation, and embodied AI at large. Plenty of approaches have been explored towards the goal of generating coherent 3D scenes, involving leveraging 2D generative priors to enable 3D generation (Chen et al. 2023] Bahmani et al. (024a) via SDS (Poole et al.) (022), building entire 3D scenes by multi-view images with NeRF (Mildenhall al.
2021) and 3DGS (Kerbl et al., 2023).
Recently, camera-guided video diffusion models have gained popularity, where image frames im- plicitly encode the scene's 3D structure with multiple images from different viewpoints, videos are natural 2D projections of dynamic 3D worlds (Huang et al.) 2025a; Cao et al. [025 He et al.)
2024 Bahmmnni et al. 2025 2024b). Such methods harness the spatiotemporal capacity of video
diffusion to improve 3D consistency and multi-view modeling, such as ReconX (Liu et al. (022a), Gen3C (Ren et al., 2025), DimensionX (Sun al., 2024). and ViewCrafter (Yu al.) 2024b). to name a few. These models excel at producing geometrically plausible local shapes and mitigating view inconsistency through temporal priors. Beyond perspective video generation, another related line of work focuses on generating panoramic 360 degree content, such as images and videos, to create immersive environments for exploration. Panoramic viewpoints provide a much larger field of view than standard perspective cameras, but they also introduce strong geometric distortions and
suffer from limited training data (Wallingford et al. 20244] Lu et al. 2024; Wang et al. 2024at
et al. 2024 Zhou et al. 2025 Xing al. 2025 Team al. 2025b;
Liu et al. 2025ap.
However, despite being trained on vast Internet video corpora and exhibiting strong imaginative pri- ors, generative video models lack explicit 3D supervision, making it difficult to preserve real-world structure and spatial consistency. This reveals a central challenge in world model construction: how inject reliable geometric grounding into video generation without sacrificing creative capacity.
Lately, the emergence of 3D foundation models, such as DUSt3R (Wang et al. (024b). MAS13R (Duisterhof al. 2025), Fast3R (Yang et al. 2025a), and VGGT (Wang et al., 2025a demon- strates that robust geometry can be predicted in a single forward pass without additional 3D recon- struction, providing a scalable solution to geometry-consiseen generation. Consequently, several works attempt to couple 2D video generation with 3D reasoning. Voyager (Huang et al. 2025a) trains an end-to-end model that jointly predicts RGB and depth while maintaining world consis- frames, and then reconstructs explorable 3D scenes.
tency through a cache and geometry-injectt Geometry Forcing (Wu al.] 2025) further marries video diffusion with an explicit 3D representa- tion to strengthen geometric consistency during training. Matrix-3D (Yang et al., (025c) shows two practical routes from omni-directional video to a navigable world, including an optimization path and a large panorama reconstruction model that directly infers 3D Gaussians from video latents. WonderWorld (Yu et al. 2025a) focuses on interactive single-image scene authoring with layered Gaussian surfels and guided depth diffusion for fast, connected scene creation. Vidu4D (Wang al., 2024d) shows that even a single generated video can support high-quality 4D reconstruction when paired with dynamic Gaussian surface elements. Complementary to these trends in content cre- ation, Gaussian World (Zuo et al. 2025)) frames perception as streaming 4D occupancy forecasting, underscoring the value of 3D world representation that can generalize across embodied tasks.
Despite significant progress in video-to-3D modeling, several limitations remain. First, most video generative models operate purely within the video domain, yielding features that cannot directly support 3D reasoning. When explicit 3D reconstruction is desired, they often resort to additional scene-specific optimization with NeRF (Mildenhall et al. (02T)) or 3DGS (Kerbl et al., 2023). which introduces computation overhead. Second, video imagination and 3D perception remain weakly coupled at inference, preventing mutual reinforcement, which effectiveness is evidenced by recent works in 3D scene understanding (Huang et al.) (025b). For instance, although Voyager (Huang jet al., 2025a) predicts RGB and depth jointly to maintain world consistency, and WorldExplorer (Schneider al. 2025) leverages video-base imagination for scene exploration, in both cases the two processes operate largely independently. highlighting the persistent limitation of weak coupling between video generation and 3D perception. Third, many approaches, such Geometry Forcing
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(Wu et al. 2025), integrate 3D priors by fine-tuning video foundation models (VFMs) while keeping a 3D model like VGGT frozen, which incurs substantial computational cost and risks compromising the VFM's general generative capacity.
To address these limitations, we introduce HAANTASWORLLE a geometry-enhaneed framework that efficiently produces reusable 3D-consistent features by augmenting frozen VFMs with an additional trainable branch for geometric inference, tightly coupling video imagination and 3D perception without expensive per-scene optimization or fine-tuning. Instead of predicting depth point clouds from RGB images, we directly infer camera parameters and 3D signals from video latents. This inspired by VGGT but achieves tighter integration between generative and geometric modeling. Concretely, we split the backbone of video foundation models (i.e., Wan2. in our case) into Precon- ditioning Blocks (PCB) that inject video priors and stabilize latents, and Integrated Reconstruction and Generation Blocks (IRG) that fuse spatiotemporal tokens with a geometry co-encoder to predict a geometry implicit 3D field. As a result, our model generates camera-conditioned video fea- tures alongside an explicit 3D representation in single forward pass, without relying on additional 3D reconstruction (e.g., NeRF or 3DGS) or iterative memory refinement as Voyager (Huang et al. 2025a)).
To this end, our contributions are as follows: TASYWORLD, a geometry-enhancee framework that jointly predicts video latents and an implicit 3D field through a single backbone, preserving imaginative priors while exposing explicit geom- etry. (ii) 2D/3D cross-branch supervision: We introduce constraints that let geometry supervise video features and video priors regularize 3D prediction, ensuring 3D-consistent frames inference. (iii) Potential for generalizable 3D features: We expect that the resulting video-3D representations serve as versatile features for downstream tasks, such as novel view synthesis and navigation, with- out per-task adaptation, which has been evidenced by recent works, such as AnySplat (Jiang et al., 2025).
(1) Unified vidco-3D modeling: We propose FAN-
RELATED WORK
2.1 FEED-FORWARD RECONSTRUCTION
Feed-forward reconstruction methods have achieved promising results in recovering 3D properties of a scene from a set of images in a single pass (Wang et al. 2024c 20256 Wang & Agapito [024]
Duisterhof et al. 2025 Tang et al. 2025 Yang al. [2025b] Zhang et al. 2024 2025b) Wang al., 2025a c). DUSI3R (Wang et al. 2024c) and MASt3 (Duisterhof et al. 2025) directly estimate point clouds from images, which suitable for challenging scenarios such as low-texture regions, but can only input two images at a time. Fast3R (Yang et al. 2025b) can process thousands of frames at once, surpassing the aforementioned restrictions. VGGT (Wang al. 2025a) introduces a more generalized framework capable of supporting input from one frame to multiple frames and produc- ingg multiple 3D attributes. Furthermore, its feature backbone serves as a versatile feature extractor for various downstream tasks. Recently, many methods have integrated diffusion knowledge into reconstruction techniques, thereby enriching the 3D modeling process with the ability to imagine occluded unobserved areas (Fu et al. 2024 Hu et al. 2025 Ke et al. 2024 Lu et al. 2025 Yang jet al. 2024| Zhu et al.] 2022) [203] Liang et al. 2025). In this work, we adopt an architecture simi- lar to VGGT as 3D implicit feature extractor. Specifically, extract implicit 3D features from WanDiT block and integrate them within a multi-task learning paradigm.
2.2 GEOMETRY-AWARE VIDEO GENERATION
The consistency of generated videos with the real physical world is crucial challenge in simula- tion. Many previous works have attempted to incorporate geometric constraints during the video generation process, primarily categorized into explicit guidance and implicit guidance. For explicit guidance, many prior works explicitly incorporate 3D signals (such as point clouds, mesh, etc.) obtained from the first frame into the model, thereby demonstrably improving generation consis- tency (Yu et al., 2024a Huang et al. 2025a: Yang et al. 2025d Cao et al., 2025). These methods often suffer from insufficient point cloud accuracy and limited scope when subjected to significant
viewpoint changes. For implicit guidance, many prior works investigate methods to enable models
to perceive 3D structural information within the diffusion process (Zhang et al. 2025a Team al.
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2025a Wu et al. 2025). Seometry-Forcinne (Wu et al. 2025) aligns the model's intermediate rep- resentations with the features of a pre-trained geometric foundational model, risks undermining the creativity of large-scale video generation models trained massive video data.
2.3 JOINT 3D AND VIDEO GENERATION
Integrating both video and 3D structural information enables the generation of comprehensive 3D scenes for applications in embodied AI and simultaneously facilitates better representation of underlying physical world. AETHER (Team et al., 2025a) unifies RGB-depth modeling and couples reconstruction with video generation. DeepVerse (Chen et al. (225a) achieves interactive joint generation via an autoregressive paradigm and control via text-specified control signals. Voy- ager (Huang al. 2025a) incorporates point cloud projection within its joint generation framework to achieve camera control.
In this paper, introduce implicit 3D feature encoding branch designed to decode various 3D attributes. Through mutual enhancement achieved during the diffusion process, it simultancously yields video features and 3D structural features.
METHODOLOGY
3.1 OVERVIEW
FANTASY is unified feed-forward model for joint video and 3D scene generation. Given reference image, an optional text prompt, and a target camera trajectory, the model produces a video aligned with the specified views while simultaneously constructing implicit 3D representation. Inputs are encoded by pretrained backbones: CLIP (Radford er al. (021) for images, umT5 (Chung al. 2023) for text, and learned camera encoder, following Wan't Plücker-ray design (Wan al., 2025 for camera poses. These signals jointly condition both the video and geometry branches during training and inference.
As shown in Fig. 2 (a), the front end employs Preconditioning Blocks (PCBs) that reuse the frozen WanDiT denoiser supply partially denoised latents, ensuring the geometry pathway operates on meaningful features rather than pure noise. The backbone then consists of stacked Integrated Re- construction and Generation (IRG) Blocks, which iteratively refine video latents and geometry fea- tures under multimodal conditioning. Each IRG block contains an asymmetric dual-branch struc- ture (Fig. 2 (b)): an Imagination Prior Branch for appearance synthesis and Geometry- Consistent Branch for explicit 3D reasoning, coupled through lightweight adapters and cross attention.
The outputs are twofold. The imagination branch generates geometry-consiseee video frames along the trajectory, while the geometry branch produces task-agnostic 3D features decoded by DPT heads into depth maps, point maps, and camera poses. This unified design supports downstream tasks such novel view synthesis, pose estimation, and depth prediction, all without per-scene optimization.
In summary, FANTASY WORLD bridges generative video priors with structured geometric reasoning in single forward pass, producing outputs that are both photorealistic and geometrically consistent.
3.2 PRECONDITIONING BLOCKS
Denoising diffusion models progressively remove noise across timesteps, revealing structure and details in the signal. Recent theory (Han et al. 2025) shows that the denoising objective balances learning signal and noise, enabling structural information emerge gradually- empirical observation that features become more informative denoising unfolds. We further ob- serve a similar effect along network depth: even at a fixed timestep, deeper WanDiT layers produce clearer spatial structure (Fig. 3) suggesting that denoising progresses across both time and depth.
with the
Motivated by this, we introduce Preconditioning Blocks (PCBs) at the front end of our framework. We reuse the first 16 frozen layers of Wan2. following its camera conditioning design, partially denoise video latents. This ensures that inputs to the geometry branch contain geometry-relevant cues rather than pure noise, reducing gradient variance and avoiding early training dominated by
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Deakier
Crara Alga
Canarr
Fram Morele
Hard
Daren Powes
Imagkases Frie: GemuttyCealnst
Buac
Brank
Clarco Erjecirr
(a) The Pipeline of F Vantasy World
World
(b) IRG Blocks
dual branch
Figure 2: Overview of FANTASY WORLD. Inputs (image, text, camera) are processed by PCBs and stacked IRG blocks, where an asymmetric dual-branch design couples video synthesis with 3D reasoning. The model outputs geometry video frames and task-agnostic 3D features.
Network Depth
Denoising Direction
the IRG latents
Figure 3: PCA over timestep-block pairs: rows vary timesteps top - bottom, columns vary blocks left -> right; the red rectangle marks the IRG input latents.
high-noise latents. PCBs thus bridge noisy initialization and acometry-awart processing, providing stable supervision from the start and allowing the geometry branch focus refining structure.
3.3 INTEGRATED RECONSTRUCTION AND GENERATION BLOOKS
At the core FANTASYWORLD are the Integrated Reconstruction and Generation (IRG) Blocks, stacked the fundamental units of the model. Each IRG block adopts asymmetric dual-brande design (Fig. (b)): the Imagination Prior Branch reuses the pretrained Wan2. backbone to propa- gate appearance-rich spatiotemporal features, while the Crcometry-Consiine into a eelmeery-aligned latent space. Unlike VGGT, which relied on DINO features, we bridge the geometry pathway Wan latents, ensuring geometry inferred in the same domain as video synthesis and avoiding feature mismatch.
Branch projects them
IRG blocks unify the branches through bidirectional cross-attention (MM-BiCrossAttn (Liu et al. 2024b, (025b)). Here, geometric cues regularize video features for multi-view coherence, while video priors provide imaginative signals that complete occluded regions and refine geometry. As cooperative units, stacked IRG blocks progressively enhance both video latents and geometry features, forming the core mechanism where imagination and structure converge.
The geometry branch outputs an implicit representation decoded by custom 3D DPT head. This head performs temporal decoding aligned with the WanVAE video frames. Motivated by the depth- wise emergence of structure observed in Sec. 3.2 the head inverts the conventional reassemble strat- egy: instead of drawing fine features from early encoder layers dominated by noise, it sources them from late diffusion blocks where semantics stronger and denoising is mature. Anchoring predic- tions in these stable features improves depth accuracy, stabilizes pose estimation, and enhances the consistency of the implicit 3D field.
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3.4 BRIDGE TO UNIFY TRAINING
Two-stage framework. Training in WORLD follows two stages: first bridging the ge- ometry branch to the Wan feature space, then unifying geometry and video through bidirectional cross-attention. Stage freezes the Wan2.1 1 backbone and adapts the geometry branch to consume hidden features; Stage 2 introduces cross-brance adapters for joint optimization, while keeping the backbone frozen. Please refer to A.2
A.3
A.4
for more implementation and training details.
Stage 1: Latent Bridging. We select hidden features from block 16 of Wan2.1 and feed them the geometry branch through a lightweight transformer adapter that maps to a reometry-aligna latent space. This latent is encoded and decoded into camera, depth, and point map predictions, while only the geometry branch is trained. Supervision is applied via:
Ege = Qt deepth + B pumpp + Ceamera
where Liepth follows Video Depth Anything (Chen et al. 2025b), Lpmpp follows VGGT (Wang al. 2025a). and Leamers is Huber penalty. This stage ensures the geometry branch operates stably on Wan latents instead of raw noise.
Stage Unified Co-Optimization. From block 16 onward, we insert one bidirectional cross- attention adapter after each of 24 transformer blocks, aligned with the geometry branch. For video tokens X, and geometry tokens Xg. with projections (Qu, K. Vo) and (Qg, Kg. Vg). attention is:
K vah
A softmax
and updates are:
x 7v Av
X - -
with learnable gates Tes To-Tg- Geometry-to-videe updates enforce 3D consistency, while video-to- geometry updates inject generative priors. Camera parameters are embedded with a pose en- coder (Wan et al. 2025); we modify the pose adapter to predict only the shift 8,, injected additively:
fi = fi-1 Bi,
applied to the first 24 of 40 blocks.
Training objective. The final objective is:
Liotal = Es [1ea -e13] ACgeo
combining the standard diffusion loss with geometry supervision which aggregates depth, point map, and camera supervision. The weight À balances video generation and geometry learning, enforcing multi-view coherence and enabling cross-branch co- adaptation.
4 EXPERIMENT
We conduct comprehensive experiments to evaluate FANTASYWORLD across large-scale bench- marks and diverse scenarios. Sec. [.1 outlines datasets, training protocols, and evaluation settings. Sec. 4.2 assesses world generation, including comparisons with state-of-the-art baselines, analysis under varying camera motions, and ablations on the geometry branch. See. exammnnss geometric fidelity through quantitative reconstruction metrics and qualitative visualizations, further validating the role of explicit geometry modeling.
4.1 IMPLEMENTATION DETAILS
Datasets. Our training corpus uses approximately 180k video clips from a diverse mix of real- world and simulated sources, with geometric supervision obtained via multiple strategies. For RealEstate 10K (Zhou et al. 2018) and ACID (Liu et al. 2021). we generate multi view consistent depth maps using reconstruction based pipeline. For DL3DV (Ling et al. 2024), WildRGB (ia (et al., 2024). ScanNet (Dai et al. 2017), and the simulated TartanAir Waan et al. (020), we instead apply Cut3R (Wang et al. 2025b) to extract geometric labels.

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Training Details, We train the model with AdamW and a learning rate of 10-5 in two stages. In Stage (latent bridging). the geometry branch is trained for 20,000 steps with a global batch size of 64, while the video backbone remains frozen, adapting the geometry branch to its feature space. In Stage 2 (unified co-optimization), training uses 81 frame clips at 592 x 336 and 336 x 592. Only the lightweight interaction modules (bidirectional cross attention and camera control adapter) are fine tuned for 10,000 steps with a global batch size of 112, with all core backbones frozen. Stage runs on 64 H20 GPUs for 36 hours, and Stage 2 on 112 H20 GPUs for 144 hours.
Evaluation. We conduct evaluation on 1,000 samples drawn from the photorealistic subset of the WorldScore static benchmark (Duan et al. 2025). The static split of WorldScore divided into pho- torealistic and stylized subsets. Since our objective is to model real-world environments for embod- ied intelligence, we focus on photorealistic video synthesis. In contrast, evaluating 3D consistency in stylized videos is ill-defined and less representative of performance in realistic scenarios, and is therefore excluded from our study. We assess our model along two complementary dimensions:
For a holistic evaluation of controllable world generation, we report perfor- mance on the WorldScore benchmark, which measures performance across multiple axes, includ- ing camera and object control, content alignment, 3D consistency, and perceptual quality. Geometric Fidelity: To quantify geometric fidelity, we reconstruct each scene with 3DGS, and report PSNR, SSIM, and LPIPS.
World Generation:
Together, these metrics capture both the controllability of video-based world generation and the structural accuracy of 3D geometry. For more evaluation details, please refer to A.5
4.2 WORLD GENERATION
We evaluate the video generation capabilities of FANTASYWORLD by comparing it against Won- derWorld (Yu al.] 2025a). Uni3C (Cao et al.) (025). Voyager (Huang et al.) (2025a), and AETHER (Team et al. 2025a) which represent the most relevant state-of-the-art baselines for 3D world generation.
Quantitative Comparison. We evaluate under two camera- motion settings. The Small configura- tion follows the WorldScore static benchmark, using 1,000 photorealistic samples to ensure consis- tency with prior work. We additionally introduce Large setting with 100 curated cases featuring wide orbital or panning trajectories (up to 90%), to test robustness under challenging viewpoints. As shown in Table I KANTASYWORLA achieves the highest scores on all consistency-related metrics (3D Consist., Photo Consist., and Style Consist.). Moreover, it yields the lowest standard deviation across samples, suggesting that our method is not only more accurate stable across diverse scenes. In the Large setting, the gains are particularly pronounced, demon- strating strong geometric stability even under substantial viewpoint shifts. Compared to baselines that prioritize instruction following and camera manipulation YYu er al. (202a)), our framework fo- cuses instead embedding geometric awareness into video features. Although this design does not directly optimize camera or content- alligmmet benchmarks, it enables stable and reusable 3D rep- resentations (our main focus), and can be extended with post-hoc explicit reconstruction to recover strong camera controllability when needed (see Appendix A.6
average but also more
Qualitative Analysis. Under large camera motion, distinct failures emerge: WonderWorld shows tearing and holes indicative of missing geometry; in Uni3C and Voyager, first-frame point-cloud priors quickly fall out of view, causing style drift in Uni3C and multi-view misalignment in Voyager, AETHER, despite generating RGB-D point clouds, often produces incoherent, low-detail content (see Fig. 4. In contrast, FANTASY WORLD predicts an implicit 3D representation that evolves with the video, ensuring stable geometry and appearance across time and yielding coherent, 3D-consistent results without the failures of static priors
Ablation on the Geometry Branch. our full model with a variant where the geometry branch and bidirectional cross-attention are re- moved. As shown in Table removing the geometry branch leads to declines in Photo Consist. and Style Consist., and an especially severe drop in 3D Consist. underscoring its critical role in ensuring multi-view coherence and high-fidelity generation.
To assess the role of explicit geometry modeling, we compare
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Table I: WorldScore with Small vs. Large camera motion.
3D
Photo Consist.
Style Consist.
Camera Ctrl.
Object Ctrl.
Content Align.
Subjective Qual
Method
Motion
Consist.
WonderWorld
82.85
23.86+22356
$5.79
92.32
47.63
79.09
69.03
AETHER
79.88 14.64
38.68
72.09
57.44
52.26
28.06
41.11
Uni3C
78.594:21.08
25.48
18.42
62.94
45.83
47,40
57.00
Small
Voyager
26.00:126.3
80.68
72.89
45.92
57.69
48.36
44.74
Ours w/o 3D
79.77:1.16.6
83.8
12.94
57.94
37.33
43,31
55.85
Ours w/ 3D
83.31++14244
86.11 1::99
94.22
57.05
34.46
38.45
57.40
WonderWorld
63.704-24.7
8.22
35,95 #33.47
96.28
38.61
97.10
72.46
AETHER
63.97:1:7.39
33.1 1:2399
61.99 1.3.24
4.43
34.78
33.69
35.09
Uni3C
73.95
46.78+:3264
71.43
8.69
34.28
77.88
51.12
Large
Voyager
13.82:11996
9.52
61,34 155.29
0.00
49.23
64.10
39.21
Ours w/o 3D
72.06-4:20.14
56.98 1:2300
81.59 2:22.3
9.32
34.44
75.85
46.96
Ours
14.83++1.31
60.61:21.22
12.02
11.24
31.96
77.20
50.46
Figure 4: Qualitative comparison of world generation. WonderWorld shows missing regions, Voy- ager suffers from temporal incoherence and degraded first-frame fidelity, AETHER produces low- detail outputs, and Uni3C exhibits abrupt stylistic shifts. In contrast, FANTASYWORKE maintains stronger 3D consistency and coherent style across views.
4.3 GEOMETRIC FIDELITY
Quantitative Comparison. We evaluate video-geometry consistency using 3DGS (Kerbl et al. 2023) on 100 RealEstate samples, comparing three settings (Tab, 2 removing the geometry branch with VGGT initialization, our full model with VGGT initialization, and full model with feed-forward point-cloud initialization. Results show that, under the same VGGT initialization, adding the geometry branch consistently improves PSNR/SSIM and reduces LPIPS, confirming its role in enforcing 3D consistency. Direct initialization from our predicted point clouds yields slightly lower scores than VGGT initialization, but still provides competitive results, indicating that geometry branch produces meaningful 3D structure without relying external supervision.
Qualitative Analysis. We compare reconstructed 3D scenes from Voyager, AETHER, Uni3C, and method on indoor and outdoor cases (Fig 5). All methods use the same pipeline: VGGT predicts point maps from generated frames, then reconstructs point clouds. Baselines show structural artifacts and duplication (e.g., blurred signage, layered walls, distorted layouts), while method yields cleaner, more complete geometry. Indoors, walls and corners stay rectilincar and closed; outdoors, text and facades sharper, indicating stronger 3D consistency.
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Table 3DGS reconstruction on RealEstate Post-reconstrrctionn (Post Rec) indicates the 3DGS initialization source: either from the VGGT point cloud or our own feed-forward point cloud.
Method
Post Rec.
PSNR t
SSIM f
LPIPS
Ours w/o 3D
VGGT VGGT
26.89
0.84
0.17
Ours 3D
28.24
0.86
0.14
Ours w/ 3D
26.54
0.85
0.19
Ablation on the Geometry Branch. As shown in Table the same geometry-ablated variant de- scribed in Sec. 4.2 produces weaker reconstruction quality, while the full FANTASY WORLD achieves clearer geometry and thus confirms the geometry branch is crucial for robust 3D representation.
Top View
Isametric View
Top View
hometticc View
Spuctaad Anliss
hooeetric View Top View
N
Voyager
AETHER
Uni3C
FantasyWorld World
Figure 5: Qualitative Comparison of Geometry Fidelity. Top-view and isometric-vier point clouds from Voyager, AETHER, Uni3C, and our method. The red-outlined regions indicate typical struc- tural artifacts in baselines (e.g., duplicated walls, bent surfaces, blurred text), while method preserves cleaner layouts and sharper geometry.
5 CONCLUSION AND LIMITATIONS
In this work, we presented FANTASYWORLD a unified feed-forward model designed to generate 3D-consistent, explorable virtual worlds single pass. Our core contribution is novel architec- ture that bridges the imaginative power of a pre-trained video diffusion backbone with the geometric rigor of a VGGT-style 3D geometry branch. To achieve this, we separate the prediction of 3D ge- ometry from video appearance, introducing a dedicated geometry branch that operates on the video model's s internal features while preserving its powerful generative capabilities. This facilitated by bidirectional cross-attention mechanism that allows video and geometry to mutually reinforce one another during generation. Our experiments show that the generated videos not only maintain strong visual realism but also achieve higher multi-view coherence and improved geometric fidelity com- pared to existing methods, offering practical and efficient path toward creating structured, reusable world models for embodied AI.
Our model is currently designed for fixed-length clip generation, and extending it to continuous, long-range synthesis remains important next step. Achieving this will require developing ef-

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Published as a conference paper at ICLR 2026
fective caching streaming mechanisms for our implicit 3D representation maintain state time, as partially explored recent work such as time, the present study focuses static quasi-static scenes where geometry approximately rigid within the generated window. Handling fully dynamic environments with strong non-rigid motion and time-varying structure remains open limitation of our current instantiation, and will require both richer data and explicit modeling of 4D geometry, for example through time-conditioned ge- ometry fields or deformation modules inspired by recent 4D extensions of VGGT-style backbones. Our architectural design is compatible with such extensions, but systematically validating them is left for future work. In contrast, our primary focus in this paper has been on validating the potential of large video diffusion backbones to serve as world models when paired with geometric supervi- sion. This design choice allowed us to clearly demonstrate our central hypothesis: that an implicit 3D representation, learned directly from video model's 's hidden features, is powerful and effective tool for enforcing 3D consistency, while opening path toward Longer-horizon and fully dynamic world modeling in subsequent work.
(Yu et al, (023b). b. At the same
REPRODUCIBILITY STATEMENT
We ensure reproducibility by detailing FANTASYWORLD'S architecture (PCB, IRG, bidirectional
cross-attention) in Sec. 3 dataset composition and preprocessing in Sec. 2.1 an training proto-
cols in Sec. 4.1 and A.44 and evaluation metrics, splits, results, and ablation studies in Sec. |.c.4.1 4.2
and 4.3 Baseline code and pretrained weights are documented in A.5 and the full codebase
available at httbtttss//ggtthh
Satm//annasy-AAlle AAMAP//ataayy-woL... world
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A APPENDIX
A.1 DECLARATION ON LLM USAGE
We used large language model (LLM) aid in the preparation of this manuscript. Specifically, the LLM was employed to polish the writing, improve readability, and refine grammar and style. All technical content, including the model design, experimental methodology, and results, was con- ceived, implemented, and validated by the authors. The LLM did not contribute to the development of algorithms, the execution of experiments, or the analysis of data, Its role was limited to assisting with language clarity and presentation.
A.2 CAMERA MOTION CONTROL
For camera motion control, we largely follow the methodology of (Wan et al. 2025). which con- sists of camera pose encoder and pose adapter. Our camera pose encoder mirrors their design, transforming camera parameters into multi-level feature embeddings via Plücker coordinates and a series of convolutional operations.
Our primary modification lies in the camera pose adapter. While the original work utilizes a full Adaptive Layer Normalization (AdaLN) predict both scaling (7:) and shifting (3:) parameters, we propose a streamlined variant that exclusively generates the shifting parameters. The features are integrated into the video latent fi-1 at each layer through simple additive projection:
fr = f--1 + Bi,
where 84 is derived from the camera embeddings. Also the camera control module is adapt only the first 24 block among the 40 transformer blocks.
A.3
3D DPT HEAD ARCHITECTURE
Our model employs a custom 3D DPT head, which extends the 2D version from VGGT (Wang et al. 2025a) to process video sequences via two key innovations, as shown in Fig
First, we invert the spatial reassembly logic to align with the nature of diffusion backbones. Con- ventional DPTs, designed for standard encoders, assume that shallow layers contain high-frequency spatial details and therefore upsample them most aggressively. Diffusion models operate differently: they progressively denoise the input at each block. This means deeper blocks produce features that not only semantically richer but also less noisy and more structurally reliable. Consequently, invert the DPT logic. Our Geometry- Consistent Branch uses features from blocks (8.12,18,22 18,24 and upsamples features from the deepest layers the most, while downsampling those from the shallower ones. This anchors the fusion process in the highest-quality information the backbone provides.
Second, temporally upsample each feature stream using two sequential Temporal Blocks after the reassemble block. The design of the upsample block is inspired by the VAE decoder (Wan et al.) 2025). Each block first doubles the temporal resolution and then applies causal 3D convolution. This process results in total 4x temporal upsampling factor, transforming an input of frames into smooth output sequence of T 4(t 1) frames.
A.4 TRAINING DETAILS FOR THE GEOMETRY BRANCH
Coordinate and Scale Normalization. Let E, [R, extrinsic, We align all frames to the first frame by:
6i] E B3x4 denotes camera-from-world
F. F. FAF Fo1
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Layer
Layer
Legend
Layer
Temporal Block
Layer
Fusion
Reassemble
Figure 6: Our 3D DPT head with temporal blocks for temporal upsampling.
so that the first camera is the canonical reference. Let X R denote the world-space point at pixel frame i, and let X be its coordinates in the first frame. We further normalize the global scale using the average point radius:
(d.))V
where V is the valid-point set. The same factor is applied extrinsic translations, depths, camera- space points, and world-space points.
In practice, some datasets provide sparse geometry geometry concentrated foreground objects, which makes scale estimation from GT point clouds unstable. For such cases, we recover scale from the first frame using MoGe: we reconstruct first-frame point cloud from MoGe depth and its average radius the scale. This single scale is then applied to the whole sequence like extrinsic translations, depths, camera-space points, and world-space points.
IRG Block. IRG blocks unify the Imagination Prior Branch and the Geometry-Consiseee Branch through bidirectional cross-attention. The Imagination Prior Branch produces a spatio-temporal
feature grid X, IR (low))xC, where 21, h H I6 H 336, W 592, C, 5120, and the Geometry-Comee Consistent Branch maps X, to the implicit 3D field X, E x where C 1024, which is then decoded into depth maps, point maps, and camera poses.
Following VGGT (Wang et al. 2025a), we concatenate one learned camera token and four register tokens to the video token sequence. Because camera trajectories temporally smooth in videos, we apply a lightweight ID convolution to the camera token stream to perform temporal upsampling. For each frame /t, the geometry head predicts 9D camera parameter vector E R29 (rotation, translation, and field-of-view).
Camera Pose Prediction.
We use hidden features from the Ccoometry-Consisiee Branch's 's layers {8, 12, 18, 24) with shapes Rex(lme) The features are fed a Depth 3D DPT head and Point-Map 3D DPT head. The depth head produces E R and the point head produces x x3 together with confidence (uncertainty) map Σ² E R and = (1 1) x 4 4+1 81.
Depth and Point Map prediction
3D point map P R where h:
H
W
T w T6
Camera loss. We supervise camera parameters with robust Huber loss, where is the ground truth:
N
Leamera
g+
gi
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Depth loss. We adapt Video Depth Anything (Chen al. 2025b) and combine temporal gradient matching term with a per-frame scale-sensitive spatial loss term:
Edepth a LTGM + B Cirame.
where LTGM enforces temporal consistency of depth gradients, and Lrame measures per-frame depth without scale/shift normalization.
Point-map loss. Following VGGT ( (Wang et al. ).25a)), we penalize both point positions and local gradients, weighted by the predicted uncertainty:
o (P:- P.)l + || (VP. - -VP:-vP.)( y log 2/P
Cpmap
Total objective. The geometry branch is trained with
Ego
Ldepth
Lpmap + Ceamera
A.5 EVALUATION DETAILS
To ensure the reproducibility of our evaluation, we provide the implementation details of all base- lines considered in our benchmark. For each method, we list the official GitHub repository, the specific commit used in our experiments, and the resolution and number of frames generated per sample.
Repositories and Commits.
Voyager:
httpl///aeeeaata..oooooaoaaaaaaa 75ed6e2) (commit 54a658b)
f2221b8)
(commit 5cf1146)
Resolution and Frame Settings. We followed the official default inference settings of each base-
line:
Fantasy World (ours): 336 x 592, 81 frames
Voyager: 512 x 768, 49 frames
Uni3C: 480 x 768, frames
AETHER: 480 720, 41 frames
WonderWorld: 512 X 512, 50 frames
All baselines were run with the official inference scripts and default hyperparameters from the spective repositories.
Efficiency and GPU Peak Memory Usage.
In this section, we report inference footprint under a single A100-80GB GPU for various methods in Table 3 including GPU peak memory usage, inference time, number of inference frames, frame resolution, and an efficiency metric PPS (pixels per second) which defined as:
Width x Height x Frames
PPS
Inference Time (s)
As shown in the Table 3 Fantasy World incurs moderate overhead over Wan2. due to the geometry branch, remains comparable to Uni3C, and is more efficient than Voyager in terms of PPS.
A.6 EXTENDING CONTROLLABILIT VIA POST-HOC EXPLICIT RECONSTRUCTION
In this section, we study how Fantasy World can be extended with stronger camera controllability through post-hoc explicit reconstruction step. The main paper focuses the native controllability
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Table Comparison memory usage and efficiency across methods.
GPU Memory (GB)
Efficiency (PPS)
Inference Time
Inference Frames
Resolution
Method
(s)
(Frames)
(Wx
Wan2.1
44.91
44.793
359.70
81
592x336
Fantasy World
58.24
33,856
475.90
81
592x336
Voyager
58.59
16,914
1139.13
49
768x512
Uni3C
64.51
37,607
794.00
81
768x480
WontasyWorld
VGGT
Fantasy World
VGGT
Figure 7: Comparison of feed-forward 3D predictions from Fantasy World and VGGT. Both models produce 3D geometry in single forward pass; Fantasy World's dideo-grounded 3D branch achieves comparable reconstruction quality to VGGT.
of single-pass video-3D model, evaluated by WorldScore. However, the proposed 3D-consistent latent representation also allows explicit control modules to be attached as plug-in components when higher controllability required.
To illustrate this, we construct a two-stage pipeline on top of Fantasy Given an input image, text prompt, and camera trajectory, we first run Fantasy World to generate 3D-consistent latent trajectory, and then reconstruct Gaussian scene for each case as post-hoc explicit reconstruc- tion step. The reconstructed scene can subsequently trajectory. We evaluate this pipeline on 50 vidcos sampled from the Small Camera Motion split, which corresponds to the official WorldScore static benchmark setting used in Table [ and report the WorldScore metrics in Table 4 These results demonstrate that, once 3D-consistent latent rep- resentation available, explicit control modules can be seamlessly integrated as plug-ins to obtain high camera control without degrading, and in fact improving, overall consistency.
re-rendered along an arbitrary target camera
We intentionally keep such reconstruction and explicit control modules outside the main model for two reasons. First, they mix the contribution of our core architecture with that of additional
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Published as a conference paper at ICLR 2026
Table 4: WorldScore metries when augmenting Fantasy World with post-hoc explicit reconstruction method.
Method
3D Consist.
Photo Consist.
Style Consist.
Camera Ctrl.
WonderWorld
88.35
73.79 86.59
70.74 90.84
98.94
Fantasy World (post-hoc)
88.88
90.71
reconstruction stage, which makes attribution less clear. Second, WorldScore camera and object control are designed to capture the native controllability of video models, whereas post-hoc re- rendering partially bypasses this objective. Here focus on simple camera-control example to illustrate the extensibility of FANTASYWORI.D; exploring richer object-level control modules is a natural direction for follow-up work.
A.7 DYNAMIC AND 4D SCENARIOS
In the main paper, CantasyWorld is presented as a unified video-3D diffusion framework for gener- ating structurally consistent 3D worlds under camera motion. Our experiments therefore focus on predominantly static or quasi-static scenes, matching applications such as mapping, navigation, and novel-view synthesis where stable 3D structure and reliable geometry evaluation are the main goals.
Architecturally, FantasyWorld is naturally compatible with dynamic and 4D extensions. The video branch frozen Wan2.1 video diffusion model that encodes rich spatio-temporal information, including non-rigid motion and lighting variations, in its latent tokens. The geometry branch and IRG blocks operate on the same spatio- spatio-temporal latents via MM- BiCrossAttention, so geometry and appearance conditioned on the full clip and
restricted rigid worlds by design.
To extend from static to fully dynamic scenes, one can re- o--parmeterize the implicit field to be time- aware while keeping the IRG-based cross-branch supervision intact, for example, by using a time- conditioned geometry representation or adding a deformation representation on top of a canonical geometry capture non-rigid motion, following VGGT-style works such 4DGT (Xu al. 2025). With suitable dynamic data and supervision, the unified video-3D formulation naturally generalizes 4D settings which scene structure, appearance, motion, and camera pose evolve over time.
A.8 MORE EXAMPLES
For completeness, include supplementary qualitative results covering both 3D feed-forward pre- dictions and video generation.
Fig presents additional side-by-side comparisons between the 3D geometry predicted by ge- ometry branch and the feed-forward baseline VGGT. Both models produce 3D structure in a sin- gle forward pass without any multi-view optimization. Across broad range of indoor and out- door scenes, Fantasy World's geometry branch consistently recovers coherent spatial layouts, main- tains stable global structure, and produces smooth and continuous surfaces. These examples illus- trate that the geometric pathway of Fantasy World captures strong spatial cues directly from video- conditioned features, enabling to serve as a robust feed-forward 3D predictor within our unified video-ged geometry framework.
In addition feed-forward 3D predictions, we also provide qualitative results on scenes that are far outside Fantasy World's training distribution. These examples include stylized artwork, illustrated environments, and other pontpootorcalisticc inputs that differ significantly from the domains seen during training. The goal of these results is to demonstrate how the model responds to inputs with unusual styles or appearances. As shown in Fig the generated trajectories remain temporally stable and structurally coherent, suggesting that MantasyWorld maintains reliable video-3D representations even under substantial distribution shifts.
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Published as a conference paper at ICLR 2026
T
Figure 8: We present Fantasy World's video generations on scenes far outside the training domain. Each row shows a full trajectory (7) sampled at four canonical timestamps. Despite substantial domain shifts, the model maintains stable appearance, coherent structure, and smooth temporal transitions throughout the sequence.
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Reviews:
[{"review_id": "bIDed3Mcu1", "reviewer": "Reviewer_i2fx", "summary": "This paper proposes a framework for outputting 3D-consistent world models given a single image, text-prompt, and a camera trajectory. It addresses the problem of missing 3D consistency in world model generation from camera-guided video generation models. The proposed method uses video priors from a frozen Wan 2.1 model to generate a geometry-aware implicit 3D field using the proposed Integrated Reconstruction and Generation (IRG) Blocks that are based on bidirectional cross-attention. The model is trained on 180k video clips from real-world and synthetic data using a diffusion loss. The model is evaluated on the WorldScore static benchmark and demonstrates improvements over existing methods for consistency-based metrics.", "questions": "- Is there a reason why the camera and object control is not significant? It might help to see a few examples in the paper to understand this better.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper identifies a key problem with camera controlled video generation models and motivates the design of the proposed model with a coherent and well-justified design choice. The results are promising in terms of their main goal of improving consistency in the generated outputs. This is evident based on both qualitative and quantitative results. The ablation presented for the geometry branch support the claimed efficacy of the geometry branch to improve the consistency metrics. The robustness of the method to large camera motion is a strong result. The authors have clearly described the limitations of the method helping the reader to scope the contributions well. The details mentioned in the paper are sufficiently detailed for reproducing the method.", "weaknesses": "The paper motivated their world based on using it for AGI, which could require camera and object control. This is a major limitation of the method since the metrics are significantly lower than other methods such as WonderWorld. The evaluation is limited to static scene, even though world models in robotics setup would require handling dynamic scenes. The model is supervised using existing pre-trained models, which could be a limiting factor for generalization as the errors can propagate to the proposed trained model. Moreover, the paper claims that the latents can be useful for downstream tasks, but not much evidence was provided to support this claim.\n\nMinor:\n- Table 1 can have a common small and large sections instead of repeating the same information in each row.\n- The captions for the figures (i.e. Figure 5) need more details to guide the reader for better understanding the results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "i1oJz0CdK6", "reviewer": "Reviewer_uFyH", "summary": "FantasyWorld proposes a novel framework that unifies video generation and 3D geometric reasoning in a single feed-forward pass. The method augments a frozen video foundation model (Wan2.1) with a trainable geometric branch that predicts an implicit 3D field alongside video latents. Key innovations include: (i) Preconditioning Blocks (PCBs) that provide partially denoised features as input to the geometry branch; (ii) Integrated Reconstruction and Generation (IRG) blocks featuring asymmetric dual branches coupled via bidirectional cross-attention; and (iii) cross-branch supervision where geometry guides video synthesis and video priors regularize 3D prediction. The resulting representations support downstream tasks like novel view synthesis without per-scene optimization. Experiments on benchmarks such as WorldScore and RealEstate10K demonstrate improved multi-view coherence, style consistency, and geometric fidelity over recent baselines including Voyager, WonderWorld, and AETHER.", "questions": "1. How would FantasyWorld handle videos with significant non-rigid motion (e.g., humans, flowing cloth)? Does the implicit 3D field assume scene rigidity, and if so, how might this be extended?\n\n2. What is the latency and memory footprint of your full model vs. baselines during inference? Is the geometry branch lightweight enough for real-time applications?\n\n3. Have you tested the geometric features directly in downstream embodied tasks (e.g., as input to a navigation policy)? If not, do you plan to release the features for community benchmarking?\n\n4. Are there scenarios where cross-branch supervision leads to over-constrained generation (e.g., sacrificing visual diversity for geometric plausibility)? Can you share any observed trade-offs?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "1. Originality: Introduces a novel asymmetric dual-branch architecture that enables mutual reinforcement between video generation and 3D perception in a single forward pass—distinct from prior decoupled or iterative approaches.\n\n2. Quality: Rigorous experimental validation across multiple datasets, camera motions, and evaluation protocols; strong baselines; thorough ablations.\n\n3. Clarity: Exceptionally clear exposition of both high-level intuition and low-level implementation (e.g., inverted DPT reassembly strategy based on diffusion depth semantics).\n\n4. Significance: Addresses a core limitation in current video foundation models—their lack of explicit 3D grounding—and provides a practical solution with immediate applicability to navigation, simulation, and content creation.", "weaknesses": "1. Limited dynamic scene evaluation: All experiments focus on static or near-static scenes (WorldScore static split, RealEstate10K). It remains unclear how the method handles non-rigid motion, deformable objects, or dynamic lighting—key challenges in real-world embodied settings.\n2. Computational cost: While avoiding per-scene optimization is a strength, the reported training uses 112 H20 GPUs for 144 hours in Stage 2. The inference cost (memory/time) relative to baselines is not discussed, which matters for downstream deployment.\n3. Downstream task validation is indirect: The claim that geometric latents are “versatile for downstream tasks” is supported only via 3DGS reconstruction. Direct evaluation on tasks like pose estimation, navigation planning, or policy learning would strengthen this assertion.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1239pE2CUk", "reviewer": "Reviewer_BpKm", "summary": "This paper presents FantasyWorld, a unified framework that jointly models video generation and 3D geometry prediction within a single feed-forward pipeline through cross-branch supervision. By employing bidirectional cross-attention, the model enables mutual information flow between appearance and geometry, effectively merging them into one coherent representation. The authors conduct extensive quantitative and qualitative experiments across multiple datasets, demonstrating clear improvements in 3D consistency, photorealism, and scene coherence compared to baseline models. Ablation studies further confirm that both the geometry branch and the cross-branch coupling are essential for performance gains, highlighting the core innovation and technical contribution of the work.", "questions": "1. I am curious about how the proposed framework can be used to generate explicit 3D structures, such as meshes or 3D Gaussian splattings (3DGS), from its implicit representation. Have the authors attempted to extract these forms of geometry using the predicted depth, point maps, or other intermediate signals? It would be helpful to see qualitative or quantitative comparisons of such reconstructions, as they directly reflect how well the learned implicit field captures structural consistency and could provide valuable insights for robotics applications.\n2. How does the proposed model behave in dynamic or non-rigid environments (e.g., moving objects, people, or changing lighting)? It would be acceptable if artifacts appear, but I am interested in whether the framework is fundamentally capable of reasoning about such dynamics, since the model takes advantages from the video foundation model.\n3. How does the method handle metric scale issues in the reconstructed geometry? Since scale ambiguity is a common problem in monocular video-based 3D reconstruction, does the proposed implicit 3D representation preserve or estimate absolute scale, and if not, how might this affect potential applications in embodied or robotic settings?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. Well-motivated and clearly written: The paper is well motivated and easy to follow. The problem formulation and technical pipeline are clearly described, making the overall logic coherent and accessible.\n2. Intuitive and meaningful innovation: The proposed model design makes sense from the first intuition — the unified modeling of video generation and 3D geometry prediction is conceptually appealing and addresses an important gap between appearance synthesis and spatial reasoning.\n3. Effective cross-attention mechanism: The introduced bidirectional cross-attention effectively facilitates mutual information merging between geometry and appearance branches.\n4. Comprehensive validation: The experiments in the manuscript, together with the demonstration video, provide convincing evidence that supports the claimed conclusions. The results are consistent across datasets.\n5. Code reproducibility and openness:The released codebase and structured training setup make the method reproductive, which is valuable for the research community and enhances the paper’s credibility.", "weaknesses": "1. Lack of theoretical justification for cross-branch supervision: Although cross-attention mechanism effectively fuses geometry and appearance features, the underlying mechanism is only demonstrated empirically. A more theoretical or analytical explanation of why this coupling improves stability and consistency would strengthen the contribution.\n2. Limited temporal scalability: As illustrated in the modeling and discussed in the Section 5, the model is designed for short, fixed-length video generation and does not address long-term temporal consistency or camera trajectory drift, which limits its generalization to continuous video required by downstream applications such as data expansion in autonomous driving and robotics.\n3. The qualitative results for 3D reconstruction are relatively limited. It would strengthen the paper if the authors could provide more visual examples, especially on out-of-domain or unseen scenarios. For instance, testing the model on stylistically distinct or challenging inputs such as artistic or highly textured scenes (e.g., Van Gogh’s paintings)  would better demonstrate the model’s generalization and robustness in geometry-aware reconstruction.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "OOhzbjYJVG", "reviewer": "Reviewer_Hacb", "summary": "This paper tries to make camera-controlled videos that stay consistent by giving the generator an internal sense of 3D. It keeps the front part of a standard video model fixed to lightly clean the input and mix in text, image, and camera hints. The main part has two paths: one draws the video frames, the other tracks the scene’s 3D shape. The two paths talk to each other, and a small module reads the 3D path to output depth, a set of 3D points, and the camera’s position and direction.", "questions": "Please provide: \n\n1) Missing ablations: \n- a no-adapter ablation \n- a no-unification control (separate backbones, or freeze geometry branch outputs with no shared features) \n\n2) Comparisons with missing baselines/related work \n\n3) 3D geometry consistency evaluation\n\nMinor: \n\n1) The first line of the abstract starts with \"High-quality 3D world models are pivotal for embodied intelligence and Artificial General Intelligence (AGI)\"  -- why?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1) Clean unified video+3D design with a frozen VFM and a trainable geometry branch; avoids per-scene optimization\n\n2) Produces camera-conditioned video + 3D signals in one forward pass, a practical interface for downstream tasks.", "weaknesses": "1) Several parts of the paper are unclear. The overall setup is unclear. What is the implicit 3D field here: tensor shapes, spatial/temporal resolution, and how it is applied at inference? The implicit 3D field definition is unclear here, and there is no diagram to support it. PCB conditioning terminology is also confusing; it's just a frozen pre-denoiser -- why is it conditioning? \n\n2) There is no ablation isolating (a) cross-branch exchange with the geometry branch kept, or (b) the effect of a unified backbone vs a non-unified alternative. The paper’s abstract line “ablation studies confirm gains stem from the unified backbone and cross-branch information exchange” is therefore only partially supported by the evidence shown (it demonstrates the benefit of having the 3D branch + adapters together, not each factor independently). The current “w/o 3D” removes both branch and adapters, so it can’t attribute gains to the unified backbone or to cross-branch exchange.\n\n3) 3D geometry consistency is also not validated. One way to do this would be to run COLMAP on generated videos and report BA reprojection error, % registered frames, avg track length/inlier ratio, and pose ATE/RE (Absolute Trajectory Error and Reprojection Error) vs the commanded camera path. \n\n4) Missing key baselines. Add apples-to-apples comparisons to single-image→world and camera-controllable video models (Odin/360-1M[1], GenEx[2], VD3D[3]/V3D[4]) under shared camera paths/seeds; report PSNR/SSIM/LPIPS, pose ATE/RE, and identity/multi-view consistency.\n\nMinor: \n1) I also noticed that the overall resolution of the resulting frames (336 × 592) is lower compared to compared baselines\n2) The code provided for the reproducibility check is not available.  [The requested file is not found on the anonymous GitHub]\n\n\nReferences:\n\n[1] From an Image to a Scene: Learning to Imagine the World from a Million 360 Videos (NeurIPS 2024)\n\n[2] GenEx: Generating an Explorable World (ICLR 2025)\n\n[3] VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control (ICLR 2025)\n\n[4] V3D: Video Diffusion Models are Effective 3D Generators [T-PAMI 2025]", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)